Search AI Jobs
16,591 published AI roles — filter by keyword, skill, seniority or contract type.
16,591 results
Updated
Research Engineer - Distributed Training
OWN YOUR INTELLIGENCE Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team. Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own. We train open frontier models and ship the same stack to our customers. Its spans the full stack of training, deploying and continuously improving models — compute, large-scale RL, environments, sandboxes, evals, and deployment. Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Semianalysis, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet. WHAT YOU’LL WORK ON - Build and optimize the distributed training infrastructure behind our pre-training and large-scale RL training workloads by contributing to our prime-rl https://github.com/PrimeIntellect-ai/prime-rl framework. - Improve end-to-end training efficiency across compute, memory, networking, and scheduling layers. - Design and implement low-level performance optimizations, including kernels, communication paths, and runtime improvements. - Work on distributed training systems spanning data, tensor, and pipeline parallel workloads. - Help shape the architecture of our RL training stack, including async rollout and post-training systems. - Contribute to open-source libraries and internal infrastructure used for frontier-scale model training. - Collaborate closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements. - Stay at the frontier of training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques. YOU MAY BE A FIT IF YOU HAVE - Strong systems engineering experience in AI/ML infrastructure, especially around large-scale model training or inference. - Deep familiarity with PyTorch and distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray, or related tooling. - Experience optimizing training performance across kernels, memory movement, communication overhead, or parallelization strategy. - Hands-on experience with large-scale training techniques including data parallelism, tensor parallelism, and pipeline parallelism. - Strong understanding of GPU architecture, profiling, and performance debugging. - Ability to identify bottlenecks across the stack and drive improvements from first principles. - Comfort working in a fast-moving environment with ambiguous problems and high ownership. ESPECIALLY EXCITING - Experience writing or optimizing CUDA / Triton kernels. - Experience with compiler or runtime optimization for ML systems. - Experience working on RL training infrastructure, rollout systems, or asynchronous training pipelines. - Experience with multi-node GPU clusters and high-performance networking. - Contributions to open-source ML systems or infrastructure projects. - Interest in publishing technical work or sharing insights through engineering blogs and technical writing. BENEFITS & PERKS - Cash Compensation Range of $150-350k, plus equity incentives, aligning your success with the growth and impact of Prime Intellect. - Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco. - Visa sponsorship and relocation assistance for international candidates. - Quarterly team off-sites, hackathons, conferences and learning opportunities. - Opportunity to work with a talented, hard-working and mission-driven team, united by a shared passion for leveraging technology to accelerate science and AI. If you’re excited about building the systems foundation for frontier-scale training and open superintelligence, we’d love to hear from you.
Applied Research - RL & Agents
OWN YOUR INTELLIGENCE Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team. Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own. Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet. ROLE IMPACT This is a role at the intersection of cutting-edge RL/post-training methods and applied agent systems. You’ll have a direct impact on shaping how advanced models are aligned, deployed, and used in the real world by: - Advancing Agent Capabilities: Designing and iterating on next-generation AI agents that tackle real workloads—workflow automation, reasoning-intensive tasks, and decision-making at scale. - Building Robust Infrastructure: Developing the systems and frameworks that enable these agents to operate reliably, efficiently, and at massive scale. - Bridge Between Applications & Research: Translate ambiguous objectives into clear technical requirements that guide product and research priorities. - Prototype in the Field: Rapidly design and deploy agents, evals, and harnesses for real-world tasks to validate solutions. Application-Driven Research & Infrastructure - Shape the direction and feature set for verifiers, the Environments Hub, training services, and other research platform offerings. - Build high‑quality examples, reference implementations, and “recipes” that make it easy for others to extend the stack. - Prototype agents and eval harnesses tailored to real-world use cases and external systems. - Pair with technical end‑users (research teams, infra‑heavy customers, open‑source contributors) to design environments, evals, and verifiers that reflect real workloads. Post-training & Reinforcement Learning - Design and implement novel RL and post-training methods (RLHF, RLVR, GRPO, etc.) to align large models with domain-specific tasks. - Build evaluations and harnesses and to measure reasoning, robustness, and agentic behavior in real-world workflows. - Prototype multi-agent and memory-augmented systems to expand capabilities for downstream applications. - Experiment with post-training recipes to optimize downstream performance. Agent Development & Infrastructure - Rapidly prototype and iterate on AI agents for automation, workflow orchestration, and decision-making. - Extend and integrate with agent frameworks to support evolving feature requests and performance requirements. - Architect and maintain distributed training/inference pipelines, ensuring scalability and cost efficiency. - Develop observability and monitoring (Prometheus, Grafana, tracing) to ensure reliability and performance in production deployments. REQUIREMENTS - Strong background in machine learning engineering, with experience in post-training, RL, or large-scale model alignment. - Experience with agent frameworks and tooling (e.g. DSPy, LangGraph, MCP, Stagehand). - Familiarity with distributed training/inference frameworks (e.g., vLLM, sglang, Accelerate, Ray, Torch). - Track record of research contributions (publications, open-source contributions, benchmarks) in ML/RL. - Passion for advancing the state-of-the-art in reasoning and building practical, agentic AI systems. - Strong technical writing abilities (documentation, blogs, papers) and research taste. - Eagerness to drive collaborations with external partners and engage with the broader open-source community. NICE-TO-HAVES - Experience with web programming (React, TypeScript, Next.js). - Experience running LLM evaluations and/or synthetic data generation. - Experience deploying containerized systems at scale (Docker, Kubernetes, Terraform). WHAT WE OFFER - Cash Compensation Range of $150-300k + equity incentives - Flexible Work (San Francisco or hybrid-remote) - Visa Sponsorship & relocation support - Professional Development budget - Team Off-sites & conference attendance GROWTH OPPORTUNITY You’ll join a mission-driven team working at the frontier of open, superintelligence infra. In this role, you’ll have the opportunity to: - Shape the evolution of agent-driven solutions—from research breakthroughs to production systems used by real customers. - Collaborate with leading researchers, engineers, and partners pushing the boundaries of RL and post-training. - Grow with a fast-moving organization where your contributions directly influence both the technical direction and the broader AI ecosystem. If you’re excited to move fast, build boldly, and help define how agentic AI is developed and deployed, we’d love to hear from you. Ready to build the open superintelligence infrastructure of tomorrow? Apply now to help us make powerful, open AGI accessible to everyone.
Applied Research - Forward-Deployed
OWN YOUR INTELLIGENCE Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team. Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own. Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet. ABOUT THE ROLE We're looking for a Forward-Deployed Research Engineer (FDRE) to serve as the primary technical interface between Prime Intellect and our most important customers: AI companies, research labs, and enterprises running post-training and agentic RL on our platform. This is not a traditional research role. You'll spend most of your time embedded with customers, understanding their models, workflows, and goals. Then, you'll translate those objectives into concrete training runs, environment designs, evaluation harnesses, and deployment recipes using the Lab stack. You are the person who makes the platform work in practice for real workloads. You'll work closely with our research, product, and infrastructure teams to feed field insights back into the platform, shaping what we build next based on what customers actually need. WHAT YOU'LL DO CUSTOMER ENGAGEMENT & TECHNICAL DELIVERY - Embed directly with strategic customers to understand their agent architectures, failure modes, and product goals - Design and build custom RL environments, evaluation harnesses, and verifiers that capture what "good" looks like for each customer's domain - Architect agent scaffolding — tool use, multi-step reasoning, memory, sandbox execution — tailored to customer workflows - Configure and launch training runs on Lab, iterating on reward functions, rollout strategies, and evaluation criteria - Serve as the technical lead for engagements end-to-end: from discovery through deployed, improved models PLATFORM FEEDBACK & ECOSYSTEM - Identify repeatable patterns from customer engagements and codify them into reference implementations, templates, and documentation - Serve as the voice of the customer internally, shaping the roadmap for Lab, verifiers, the Environments Hub, and training infrastructure - Build high-quality examples and "recipes" that make it easy for new customers and open-source contributors to extend the stack - Contribute to technical content (blog posts, tutorials, case studies) that demonstrates real-world platform usage APPLIED RESEARCH & EXPERIMENTATION - Develop novel evaluation methodologies for agentic behavior — multi-step reasoning, tool use correctness, recovery from failure, long-horizon task completion - Prototype and iterate on agent harnesses for real-world tasks: code generation, workflow automation, document processing, and more - Experiment with reward design, rubric construction, and environment shaping to improve training signal quality - Stay current on the frontier of agentic AI, evals, and post-training methods, and bring that knowledge directly into customer work WHAT WE'RE LOOKING FOR - Deep hands-on experience building, evaluating, or deploying LLM-based agents in the past 1–2 years — you've seen what breaks in production and know what good evals look like - Strong intuition for evaluation design: you can look at a customer's agent and quickly identify what to measure, how to construct a rubric, and where the reward signal is weak - Working understanding of RL and post-training concepts (GRPO, RLHF, reward modeling, SFT) — you don't need to have written a trainer from scratch, but you should understand what the knobs do and why they matter - Strong Python skills and comfort with the modern AI stack (Hugging Face, inference engines, agent frameworks) - Experience in a customer-facing or consulting-adjacent technical role, or as a technical founder — you're comfortable in a room with a customer's engineering team figuring out what to build - Excellent written and verbal communication — you can write a clear environment spec, a compelling case study, and a useful Slack message to a frustrated customer - High agency and comfort with ambiguity. You don't wait for specs; you scope the problem, ship a solution, and iterate NICE-TO-HAVES - Experience with agent frameworks and tooling (DSPy, LangGraph, MCP, Stagehand, browser automation) - Experience building or running LLM evaluation pipelines at scale (benchmarks, synthetic data generation, model grading) - Research experience — publications, open-source contributions, or benchmarks in ML/RL/agents - Familiarity with sandbox/code execution environments for agent evaluation - Web programming experience (React, TypeScript, Next.js) for building demos and customer-facing tooling WHAT WE OFFER - Cash Compensation Range of $150-300k + equity incentives - Flexible Work (San Francisco or hybrid-remote) - Visa Sponsorship & relocation support - Professional Development budget - Team Off-sites & conference attendance GROWTH OPPORTUNITY You’ll join a mission-driven team working at the frontier of open, superintelligence infra. In this role, you’ll have the opportunity to: - Shape the evolution of agent-driven solutions—from research breakthroughs to production systems used by real customers. - Collaborate with leading researchers, engineers, and partners pushing the boundaries of RL and post-training. - Grow with a fast-moving organization where your contributions directly influence both the technical direction and the broader AI ecosystem. If you’re excited to move fast, build boldly, and help define how agentic AI is developed and deployed, we’d love to hear from you. Ready to build the open superintelligence infrastructure of tomorrow? Apply now to help us make powerful, open AGI accessible to everyone.
Applied Research - Evals & Data
OWN YOUR INTELLIGENCE Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team. Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own. Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet. Role Impact This is a customer facing role at the intersection of cutting-edge RL/post-training methods, applied data, and agent systems. You’ll have a direct impact on shaping how advanced models are aligned, evaluated, deployed, and used in the real world by: - Advancing Agent Capabilities: Designing and iterating on next-generation AI agents that tackle real workloads—workflow automation, reasoning-intensive tasks, and decision-making at scale. Working with applied data from real deployments to continuously refine policies, improve reasoning, and enhance reliability and safety. - Building Robust Infrastructure: Developing the distributed systems, evaluation pipelines, and coordination frameworks that enable these agents to operate reliably, efficiently, and at massive scale. Building data capture, processing, and versioning workflows for feedback, model traces, and reward signals. - Bridge Between Customers & Research: Translating customer needs and insights from applied data into clear technical requirements that guide product and research priorities. Collaborating closely with RL and eval teams to ensure real-world signals inform model alignment and reward shaping. - Prototype in the Field: Rapidly designing and deploying agents, evals, and harnesses alongside customers to validate solutions. Using applied evaluation data to iterate on model performance and discover new capabilities. Customer-Facing Engineering - Work side-by-side with customers to deeply understand workflows, data sources, and bottlenecks. - Prototype agents, data pipelines, and eval harnesses tailored to real use cases, then hand off hardened systems to core teams. - Translate customer insights and evaluation results into roadmap and research direction. Post-training & Reinforcement Learning - Design and implement novel RL and post-training methods (RLHF, RLVR, GRPO, etc.) to align large models with domain-specific tasks. - Build evaluation harnesses and verifiers to measure reasoning, robustness, and agentic behavior in real-world workflows. - Integrate applied data collection and analytics into the post-training process to surface regressions, emergent skills, and alignment opportunities. - Prototype multi-agent and memory-augmented systems to expand capabilities for customer-facing solutions. Agent Development & Infrastructure - Rapidly prototype and iterate on AI agents for automation, workflow orchestration, and decision-making. - Extend and integrate with agent frameworks to support evolving feature requests and performance requirements. - Architect and maintain distributed training and inference pipelines, ensuring scalability and cost efficiency. - Develop observability and monitoring (Prometheus, Grafana, tracing) to ensure reliability and performance in production deployments. Requirements - Strong background in machine learning engineering, with experience in post-training, RL, or large-scale model alignment. - Experience with applied data workflows and evaluation frameworks for large models or agents (e.g., SWE-Bench, HELM, EvalFlow, internal eval pipelines). - Deep expertise in distributed training/inference frameworks (e.g., vLLM, sglang, Ray, Accelerate). - Experience deploying containerized systems at scale (Docker, Kubernetes, Terraform). - Track record of research contributions (publications, open-source contributions, benchmarks) in ML/RL. - Passion for advancing the state-of-the-art in reasoning, measurement, and building practical, agentic AI systems. What We Offer - Cash Compensation Range of $150-300k + equity incentives - Flexible Work (remote or San Francisco) - Visa Sponsorship & relocation support - Professional Development budget - Team Off-sites & conference attendance Growth Opportunity You’ll join a mission-driven team working at the frontier of open, superintelligence infra. In this role, you’ll have the opportunity to: - Shape the evolution of agent-driven, data-informed solutions—from research breakthroughs to production systems used by real customers. - Collaborate with leading researchers, engineers, and partners pushing the boundaries of RL, evaluation, and post-training. - Grow with a fast-moving organization where your contributions directly influence both the technical direction and the broader AI ecosystem. If you’re excited to move fast, build boldly, and help define how agentic AI is developed and deployed, we’d love to hear from you. Ready to build the open superintelligence infrastructure of tomorrow? Apply now to help us make powerful, open AGI accessible to everyone.
Senior Applied Engineer
About us Re-architecting Engineering for the Age of Intelligence PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area. PhysicsX is starting a research team in Singapore to build physical foundation models alongside our customers and partners, targeting engineering domains where this capability will be most transformative. What you will do - Work closely with our research scientists, simulation engineers, customers and partners to deliver AI models that address real-world physics and engineering problems. - Design and build physical foundation models with a focus on efficiently scaling model training to large data on multi-GPU cloud compute. - Transform prototypes from your research scientist colleagues into robust and optimised implementations, challenging architecture decisions that hurt scalability. - Identify and argue for the best libraries, frameworks and tools to set us up for success. - Own Research work-streams at different levels, depending on seniority. - Discuss the results and implications of your work with colleagues and customers, especially how these results can address real-world problems. - Work at the intersection of data science and software engineering to translate the results of our Research into re-usable libraries, tooling and products. - Foster curiosity and initiative among your colleagues and mentees. What you bring to the table - Enthusiasm about developing machine learning solutions, especially deep learning and/or probabilistic methods, and associated supporting software solutions for science and engineering. - Ability to work autonomously and scope and effectively deliver projects across a variety of domains. - Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly. - Excellent collaboration and communication skills — with teams and customers alike. - MSc or PhD in computer science, machine learning, applied statistics, mathematics, physics, engineering, software engineering, or a related field, with a record of experience in any of the following: - Scientific computing; - High-performance computing (CPU / GPU clusters); - Parallelised / distributed training for large / foundation models. - Ideally, >2 years of experience in a data-driven, professional setting , with exposure to: - scaling and optimising ML models, training and serving foundation models at scale (federated learning a bonus); - distributed computing frameworks (e.g., Spark, Dask) and high-performance computing frameworks (MPI, OpenMP, CUDA, Triton); - cloud computing (on hyper-scaler platforms, e.g., AWS, Azure, GCP); - building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., NumPy, SciPy, Pandas, PyTorch, JAX), especially including deep learning applications; - C/C++ for computer vision, geometry processing, or scientific computing; - software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps); - container-ization and orchestration (Docker, Kubernetes, Slurm); - writing pipelines and experiment environments, including running experiments in pipelines in a systematic way. What we offer Build what actually matters Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind. Learn alongside exceptional people Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home. Influence over hierarchy We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected. Sustainable pace, long-term ambition Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person. We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics. We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.
Senior AI / Agentic Engineer
About us Re-architecting Engineering for the Age of Intelligence PhysicsX is the physics AI company for industrials. The company’s mission is to accelerate hardware innovation by overhauling what industrial engineering and manufacturing look like today. PhysicsX is building a new simulation software stack to deliver deep physics AI enablement across the entire engineering lifecycle. The company partners with leading organisations in aerospace & defence, automotive, semiconductors, materials, and energy & renewables, supporting them on some of their most critical and complex challenges. PhysicsX is headquartered in the United Kingdom, with offices in London, New York, and Singapore and an expanding presence in the Bay Area. The Mission We’re looking for an enthusiastic and opinionated Senior AI Engineer to define how AI agents transform Engineering workflows across industries such as Manufacturing, Aerospace, and Semi-conductor. You'll be building the foundations that power next-generation simulation and design tools used by industry-leading engineering teams. Our platform allows Forward Deployed Engineers (FDEs) and customers to build and deploy deep learning surrogates that solve massive engineering challenges. Your mission is to architect the Agentic stack within this wider ecosystem. You will build a production-grade platform that enables our Product teams, FDEs, and customers to compose advanced AI workflows safely, transparently, and reliably. - You will set the strategic direction for our platform's critical infrastructure and lead key implementation efforts. - You are someone with the scar tissue of running agents in production. - You have strong, reasoned opinions on emerging open standards (such as MCP, A2A, and ACP) and deep expertise in complex architectural patterns like durable execution and agent memory. - You are passionate about building a world-class developer experience that transforms complex research into robust, deployed engineering solutions. Core Responsibilities You will serve as the principal architect for our Agentic ecosystem, responsible for the high-level design choices that define how agents run at PhysicsX. You will cover topics such as: - Agent Observability: Own the implementation to enforce deep tracing, granular cost tracking, and observability across the lifecycle. - Agent Deployment: Deliver an intuitive deployment lifecycle which simplifies questions around auth, discoverability and resource requirements. - Agent Sandboxing: Architect secure environments to isolate and allow agents to operate safely. - Agent Evals: Implement a stack that empowers domain experts to efficiently annotate traces, turning qualitative feedback into quantitative, reproducible evaluation datasets. - Agent Governance: Implement robust identity and access patterns to ensure agents can safely and correctly act on behalf of users in a regulated enterprise environment. The Tech Stack - Core Platform: Python (Primary), Go or TypeScript (Secondary), Kubernetes, Docker, Terraform. - Agentic Infrastructure: LangGraph/LangChain, Temporal (Durable Execution), Vector DBs (Pinecone/Weaviate). - Observability & Evals: OTel, LangSmith, Arize, Braintrust. Who You Are - An Architect at Heart: You have strong, reasoned opinions on Durable Execution vs. Standard Async , Vector Search vs. Keyword Search , and Prompt Management strategies . - Platform-First: You care deeply about the Developer Experience (DevEx) of the Customers, FDEs consuming your platform. You build tools that make the "right way" the "easy way." - Security-Minded: You understand the risks of allowing LLMs to execute code and access data, and you know how to mitigate them via rigid sandboxing and permissioning. Qualifications - Platform & Backend Foundations: - 4+ years of experience in Platform Engineering, Backend, or SRE. - Strong proficiency in Python/Go, Kubernetes, Docker, and IaC (Terraform). - Agentic & AI Engineering: - Production experience designing Agentic architectures (chains, tools, memory). - Familiarity with Agentic frameworks (LangGraph, PydanticAI) and patterns like durable execution. - Understanding of LLM-specific lifecycle issues: non-determinism, systematic evals, and token-based cost tracking. Bonus Points - Background in Engineering workflows or simulation platforms. - Experience building internal developer platforms (IDPs). What we offer Build what actually matters Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind. Learn alongside exceptional people Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you’re ambitious, thoughtful, and driven by impact, you’ll feel at home. Influence over hierarchy We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn’t just welcomed, it’s expected. Sustainable pace, long-term ambition Building meaningful technology is a marathon, not a sprint. We believe in balancing focused, ambitious work with a life beyond it. Our hybrid model blends time together in our Shoreditch office with work-from-home days, giving you the flexibility to work sustainably while staying connected in person. And it doesn’t stop there … 🚀 Equity options - share meaningfully in the company you’re helping to build. 🏦 10% employer pension contribution - because investing in future matters. 🍽️ Free office lunches - to keep you energised and focused. 👶 Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most. 🍼 YellowNest nursery scheme - to help working parents manage childcare costs. ☀️ 25 days of Annual Leave (+ Public Holidays) - because taking time to rest matters. 🏥 Private medical insurance - 100% employee cover, giving you complete peace of mind. 💪 Wellhub Subscription - gain access to thousands of gyms, classes and wellness apps, supporting both physical and mental wellbeing. 👀 Eye tests - because good work depends on good health. 📈 Personal development - dedicated support for learning, development, and leveling up over time. 💛 Employee Assistance Programme (EAP) - confidential wellbeing support, available whenever you need it. 🚲 Bike2Work scheme and 🚆 Season ticket loan - to make getting to work easier and greener. 🚗 Octopus EV salary sacrifice - for a simpler, more sustainable way to drive electric. 🔎 Watch this space, we’re continuing to build this as we grow… We value diversity and are committed to equal employment opportunity regardless of sex, race, religion, ethnicity, nationality, disability, age, sexual orientation or gender identity. We strongly encourage individuals from groups traditionally underrepresented in tech to apply. To help make a change, we sponsor bright women from disadvantaged backgrounds through their university degrees in science and mathematics. We collect diversity and inclusion data solely for the purpose of monitoring the effectiveness of our equal opportunities policies and ensuring compliance with employment and equality legislation. This information is confidential, used only in aggregate form, and will not influence the outcome of your application.
Research Scientist, AI RL and LLMs
NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world. Join NVIDIA's Nemotron Harness team. We are leaders in AI research, exploring machine learning, reinforcement learning, computer vision and natural language processing. As a Research Scientist, you will work on projects that seek to redefine general intelligence. This Zürich, Switzerland position offers an outstanding platform to participate in innovative research and impact the future of AI. What you will be doing: Conduct groundbreaking research to advance the science and technology of intelligent machines. Co-develop models with harnesses, increasing task horizon, improving memory and general intelligence capabilities in VLM/LLM systems. Work towards daring long-term research goals, while identifying and achieving immediate achievements. Influence the research community through high-quality publications and collaborative efforts. Open source high-quality code and ensure the reproducibility of your research. What we need to see: PhD or equivalent 6+ years of experience in Computer Science, relevant technical field, or similar, completed prior to joining NVIDIA. Research background in machine learning, artificial intelligence, computational statistics, or applied mathematics. A consistent track record of research publications demonstrating experience in theoretical or empirical research. Proficiency in Python or similar programming languages for developing and debugging. Expertise in analyzing and collecting data from various sources. Ways to stand out from the crowd: Experience in building and analyzing large-scale models for vision, language, speech, or general agents. Established in guiding the development of AI research communities. Prior experience with open-source projects. Outstanding problem-solving skills and the ability to think creatively and collaboratively. Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer to you and your family www.nvidiabenefits.com/
Research Scientist
NVIDIA is searching for outstanding AI researchers to join the NVIDIA Research Singapore team. We are dedicated to the development and optimization of GPU-accelerated efficient AI computing, covering language model, visual generation, and robotics. We focuses on pushing the boundaries of generative AI by designing models that are not only powerful but also efficient in terms of computational resources. You will be part of an amazing collaborative research team that consistently publishes at the top venues in machine learning, robotics and computer vision. Your contributions have the chance to create real impact on our products. What you'll be doing: Research, design and accelerate novel generative AI models Publish original research Collaborate with other team members and teams Mentor interns Speak at conferences and events Transfer technology to product groups Collaborate with external researchers What we need to see: Excellent knowledge of LLM and foundation models Publication at leading conferences (ie. CVPR, ICML, ICLR, ICCV, NeurIPS, etc) 2 years of experience. Bachelor, master, or PhD degree in computer science or electrical engineering with strong research track records. Excellent programming skills in some rapid prototyping environment such as Python; C++ and parallel programming (e.g., CUDA) is a plus Knowledge of common machine learning frameworks, such as PyTorch Excellent communication skills NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and productive people in the world working for us. Looking forward to hear from you.
PhD Research Intern, Autonomous Systems and Physic...
We are recruiting outstanding Research Interns to work on autonomous vehicles, robotics, and physical AI! Intelligent machines that can perceive, reason, and safely interact with people and the physical world are rapidly becoming a reality. Self-driving cars, autonomous delivery and construction vehicles, mobile manipulators, and other robotic systems are moving closer to widespread deployment. However, fundamental research challenges remain before these systems can operate safely, reliably, and autonomously in complex, unfamiliar environments. For example, how can we: Develop new training paradigms that use perception and interaction to train autonomous vehicle and robot policies in closed loop? Equip autonomous systems with online and offline assurances that meet the requirements of safety-critical applications? Enable vehicles and robots to navigate across new environments, tasks, and embodiments? Build systems that reason under uncertainty and interact safely and naturally with people and other agents? These are some of the exciting questions being explored by NVIDIA’s Autonomous Systems and Physical AI Research (ASPIRE) group. Our diverse, interdisciplinary team conducts foundational research spanning foundation model design, embodied reasoning, safety, closed-loop training and evaluation, and agentic workflows for Physical AI development. We also investigate related areas including decision-making under uncertainty, deep learning, reinforcement learning, simulation, and the verification and validation of safety-critical AI systems. Our focus is on fundamental research, and lab members are encouraged to publish their work and open-source their code. NVIDIA is known for its collaborative culture, and our researchers work closely with experts across the company in autonomous vehicles, robotics, perception, simulation, and machine learning. This creates opportunities to influence real-world products while retaining the freedom and bandwidth to conduct groundbreaking, publishable research. What you’ll be doing: Designing and implementing cutting-edge techniques for autonomous vehicles, robotics, and physical AI. Conducting original research and publishing your results. Collaborating with research colleagues, internal product teams, and external researchers. Transferring technology you’ve developed to relevant product groups. What we need to see: Currently pursuing a PhD in Robotics, Computer Science, Computer Engineering, or a related field. Relevant research experience in autonomous vehicles, robotics, embodied AI, or autonomous systems. Strong knowledge of the theory and practice of vehicle or robot autonomy—or expertise in a related area, with a strong interest in applying your work to autonomous systems. A track record of research excellence, demonstrated through publications at leading conferences and journals—such as RSS, ICRA, CoRL, IJRR, NeurIPS, ICML, CVPR, TAC, etc.—and through other research artifacts such as open-source software. Exceptional Python programming skills; experience with C++ and parallel programming, such as CUDA, is a plus. Experience with machine learning frameworks such as PyTorch. Strong communication and interpersonal skills are required, along with the ability to thrive in a dynamic, research-focused team. Are you intellectually curious, collaborative, and motivated to solve complex problems at the intersection of AI and the physical world? If so, you may be a great fit for NVIDIA.
Developer Relations Manager - Southern Europe
NVIDIA is the world leader in accelerated computing and artificial intelligence and has been powering Deep Learning, AI, accelerated data analytics, autonomous systems and robots around the world. NVIDIA's customers include global tech-titans and span multiple verticals such as Manufacturing, Healthcare, Automotive, Telco and others. We’re looking for a technical Developer Relations Manager to lead engagement with startups and software partners building AI agents and generative AI applications across numerous industries as well as work with the local ecosystem and AI communities. Your mission is to identify disruptive startups and software builders building AI agent-powered tools or native LLM/multimodal applications, and show them how NVIDIA’s platforms can boost performance and inference. You’ll work closely with startups, developers, and partners who are creating autonomous AI agents, domain-specific copilots, and intelligent applications. This role is a key member of the NVIDIA team that would manage our technical engagements in leading industries across Southern Europe. Our expectation is for a leader who can function effectively and independently in a matrixed environment to groom and guide developers. This is a regional role covering France and Southern Europe. What you will be doing: Your main responsibility will be developing a technical strategy to assure GPU and NVIDIA platforms' adoption for the selected industries focusing on priorities. You will need to establish relationships and influence the technical leaders and technical communities, specifically developers, startups and ISVs within these industries. You will evangelize and develop our leadership position in this market by accelerating the availability of GPU-accelerated AI and data Science applications in the specified market by helping developers understand the value of our hardware products and SDKs in addressing critical development opportunities. Lead participation in targeted customer and industry developer events and activities. Chair technical activities spanning product divisions and sales geographies, particularly with solution architects, software developers and engineering resources, developer marketing contribute towards our local ecosystem strategy and development of our value messaging for your customers. Be the key advisor to how we are differentiated. You will become a NVIDIA technology mentor and focal point for the software developer community. What we need to see: Bachelor's Degree in Engineering, Science, Technical or other related discipline or equivalent experience. Master's or Ph.D’s is preferred. Intellectual curiosity and passion for innovation. Experience in several verticals/industries with good knowledge and trends in the industry. Expertise in CUDA programming, GPU platforms and Deep Learning and Machine Learning frameworks. You will show a deep understanding of who and how to engage the developer’s product and engineering organizations with at least 8 years related experience. 8+ years’ experience in an AI and ML software development environment or working with developers in these areas; and at least 3 years’ experience in business development activities. Able to work independently and possess excellent communication skills to drive customer and internal engagements. Demonstrate ability to influence, evangelize and persuade at both operational and executive level (including engineering/ product management) to achieve a targeted outcome. Execute and accelerate strategic decisions Ability to effectively deliver value propositions for specific and targeted industries. Ensure a positive experience for external customers and partners while working cross functionally within our organization. Ways to stand out from the crowd: Experience working on AI Deep Learning and Machine Learning Applications, AI Model Training/Inferencing and other GPU related technologies and application domain. Strong technical understanding of Data Analytics, Conversational AI, Embedded System/Jetson. Experience in network communication protocol is an added advantage. Strong analytical, problem solving, and negotiation skills and the ability to use data analysis to support strategic decisions Excellent organizational, planning, and execution skills We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.
Software Engineer, Applied AI Infrastructure
Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Team Frontier models are fungible. Any team can rent the same intelligence we can, and the model we build on today will be replaced within a month. What is not fungible is the infrastructure that decides whether an autonomous system's output can be trusted — evaluation, verification, and the discipline to gate on evidence instead of impressions. Nuro has spent a decade building exactly that discipline for a robot that drives on public roads, and this team turns it inward: we build the platform that lets AI agents operate autonomously inside Nuro's own engineering organization, under the same standard of proof we apply to the vehicle. Our mandate is to amplify the output of every engineer and researcher at Nuro by 100x. Not a better IDE, not a faster build — a change in what a single person can attempt. That number is a target, not a claim, and reaching it depends on one thing above all: autonomous work has to be trustworthy enough to run unattended. So our central ambition is to build the most rigorous closed-loop evaluation system for AI work anywhere. Leverage follows from trust, and trust follows from measurement. We operate as a startup inside a company that has already shipped a hard thing. Small team, no established playbook, direct access to compute and to the systems we are automating. You will work directly with engineering leadership and the CEO, and the decisions you make will be yours to make rather than yours to implement. About the Role We already operate a substantial agent system in production — a fleet of agents with an extensive library of skills and plugins, integrated into the tools our engineers use daily. This role is about what it takes to make that system trustworthy, autonomous, and an order of magnitude more capable. Three things sit at the center of it. Closed-loop evaluation. Our ambition is to build the most rigorous evaluation system for AI work anywhere — closed-loop, meaning every agent action produces a measurable outcome that feeds back into whether that agent is trusted to act again. Acceptance, revert, and override rates per workflow. Statistical honesty about whether a difference is real. Regression detection that fires before a human notices. Everything else on this team depends on this being right, and almost nobody has built it well. Agent platform. The runtime that makes autonomous agents safe to run against real systems: orchestration, sandboxing and isolation, tool and skill frameworks, memory, identity and permissioning, and the gateway and observability layer underneath. Agents that touch production code and production infrastructure need containment and auditability before they need capability. Autoresearch infrastructure. The automation of the research loop itself: agents that read the current state of a model and its metrics, form a hypothesis, launch an experiment, evaluate the result honestly, and either propose a change or discard the idea and move on. At Nuro that loop runs against the training pipelines behind the driving model — real experiments, real compute budgets, real metrics that determine whether a behavior ships. The hard parts are trusting the measurement, surviving experiments that take days, spending finite research compute wisely, and producing proposals a skeptical researcher can audit and reject. Alongside this, the team builds agent-powered tooling across the engineering lifecycle — code generation, review, debugging, test and CI failure attribution, knowledge retrieval, triage. There is also appetite on this team for post-training our own models where an internal workload justifies it, and the engineer in this role would be central to that work. About the Work What You Might Own in Your First Two Quarters - Build the closed-loop measurement layer that tells us, per workflow, whether agent output is accepted, reverted, or overridden — and use it to decide where autonomy expands and where it gets pulled back. - Take the autoresearch loop from assisted to unattended for a bounded class of experiments, including the eval and confidence machinery required to run it without a human in the loop. - Design the isolation and permissioning model that lets agents act on production repositories and infrastructure with an auditable record of what they did and why. About You - 3+ years of software engineering experience (or 2+ with a Master's) in computer science, engineering, or equivalent practical experience. Staff-level candidates should bring correspondingly deeper scope and ownership. - Deep, current taste in LLM research. You understand how a model is trained from scratch — data, tokenization, architecture, pretraining dynamics, the full post-training stack of supervised fine-tuning, preference optimization, and RL — and you can reason about what a training decision does to model behavior. You follow the literature because you want to, not because it is on a roadmap. - You know what happens under the hood at inference. Attention and KV-cache behavior, batching and scheduling, quantization, speculative decoding, prefix caching, context handling, and how each trades off latency, throughput, and cost. - You have built and operated LLM-based agent systems in production — tool use, orchestration, sandboxing, retrieval, memory — and you know where they break. - Strong backend and distributed systems background at scale: cloud infrastructure, service design, storage, queuing, and the judgment to build things that stay up. - Strong programming skills in Python. - You are opinionated about evaluation. You have argued with someone about whether a benchmark measured anything real, and you were right. - You work end-to-end and do not need the problem pre-decomposed. This role has more surface than a specification. - Hands-on post-training or fine-tuning experience — SFT, preference optimization, RL, distillation — including the evaluation work required to know whether it helped. - Experience with ML training or research infrastructure: experiment orchestration, evaluation pipelines, hyperparameter search, data pipelines. - Experience running inference serving, cost, or capacity at meaningful scale. - Familiarity with agent architecture patterns: planning, reflection, long-horizon memory, multi-agent coordination. - Experience with open tool-integration protocols, plugin or skill frameworks, and model-routing or gateway layers. - Background in developer experience or platform engineering, observability, or security isolation. - Go, C++, or Rust experience in addition to Python. At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $193,930 and $352,290 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package. At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics.
Senior/Staff Machine Learning Engineer, Sensor Sim...
Who We Are Nuro believes self-driving vehicles are the most immediate and profound opportunity for AI to drive positive change in the physical world. Safer streets, more time for what matters, and easier access to the world around us, that’s why we’re building a universal autonomy platform: self-driving for all roads and all rides. Founded in 2016, Nuro is a physical AI company developing Level 4 autonomous driving technology for a wide range of vehicles, use cases, and markets. Powered by the Nuro Driver™, our universal autonomy platform enables the global mobility ecosystem to deploy autonomy at scale, from robotaxis and logistics fleets to personal vehicles. With years of real-world deployment experience and a flexible, partner-led business model, Nuro is working toward a future where millions of autonomous vehicles powered by our technology help make everyday life safer, easier, and more connected. Nuro has raised over $2B in capital from Uber, NVIDIA, Google, Softbank, Fidelity, T. Rowe Price, and other leading investors. About the Role Our robotics team is growing and we are looking for a Software Engineer to join our Sensor Data and Calibration team. We are searching for an engineer with robotics and machine learning expertise to develop synthetic sensor simulation models and algorithms. The ideal candidate has hands-on experience in the research, development, and implementation of machine learning methods (e.g., NeRF or Gaussian splatting) for generating synthetic sensor data (photorealistic images, realistic lidar and/or radar, etc.). About the Work - Research, develop, and implement state-of-the-art synthetic sensor simulation methods - Analyze and characterize the realism and utility of synthetic sensor data - Answer critical questions about sensor data and autonomy performance - Collaborate with stakeholders across autonomy, infrastructure, and systems teams on map needs and requirements - Role is scoped as a Senior/Staff IC with the flexibility to grow into a Tech Lead/TLM. About You - One of - PhD in machine learning, computer science, electrical engineering, robotics, or related field, and 3+ years of industry experience - Masters and 4+ years of industry experience - 5+ years of industry experience - Deep understanding of ML fundamentals with hands-on experience in training and evaluating modern ML models - Strong Python skills with experience in deep learning frameworks, e.g., PyTorch, TensorFlow, or Jax Bonus Points - Deep understanding of 3D geometry and state estimation fundamentals - Proficiency in systems coding - Experience in simulating/modeling real sensors (camera, lidar, radar, IMU, etc), including noise modeling - Experience in modern ML graphics techniques, e.g., NeRF, Gaussian Splatting, and/or generative models. - Experience in building ML pipelines and optimizing/productizing ML models - Demonstrated research publications in top conferences (e.g. NeurIPS, ICLR, ICML, CVPR, RSS, CoRL, ICRA) At Nuro, your base pay is one part of your total compensation package. For this position, the reasonably expected base pay range is between $193,930 and $291,150 for the level at which this job has been scoped. Your base pay will depend on several factors, including your experience, qualifications, education, location, and skills. In the event that you are considered for a different level, a higher or lower pay range would apply. This position is also eligible for an annual performance bonus, equity, and a competitive benefits package. At Nuro, we celebrate differences and are committed to a diverse workplace that fosters inclusion and psychological safety for all employees. Nuro is proud to be an equal opportunity employer and expressly prohibits any form of workplace discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other legally protected characteristics. #LI-DNP
Senior AI/ML Software Engineer
ABOUT NABLA We are a team of entrepreneurs, clinicians and engineers committed to bringing back joy to the practice of medicine. Together with a community of clinician innovators, we’ve harnessed the best of machine learning science to develop Nabla: the leading AI assistant that’s restoring the human connection at the heart of healthcare. By streamlining clinical documentation, Nabla is helping clinicians focus on what matters most - patient care. Today, over 100,000+ clinicians across 130+ healthcare organizations trust Nabla to support how they deliver care every day. We’re at the start of an ambitious journey: Ambient listening, dictation, coding, and command capabilities are all converging into a proactive assistant that intuitively streamlines clinical and financial workflows. Backed by a recent $70M Series C, we’re hiring to build the next generation of clinical AI and improve the lives of clinicians and patients everywhere. This is a great time to join us! THE BEST OF AI AT THE SERVICE OF HEALTHCARE Led by former Meta AI Research engineers, our team has consistently anticipated how AI can revolutionize healthcare delivery. Our Machine Learning team continually leverages the latest advancements to unlock AI’s full potential in healthcare. Yann LeCun, Meta’s ex-Chief AI Scientist and Turing award winner, is an advisor to Nabla. He’s now focused on AMI Labs, with whom we have a close partnership. ENGINEERING AT NABLA Engineering at Nabla is lean, fast-moving, and deeply technical. Our teams span machine learning, native desktop applications, and platform infrastructure to deliver AI into clinical settings reliably and at scale. YOUR TEAM At Nabla, product development is led jointly by Engineering and Product, and organized into cross-functional squads, each focused on a specific product area. Squads bring together product managers, designers, engineers of different profiles, and domain experts, and operate with a high level of ownership. As an AI Engineer, you’ll join one of these squads and contribute end to end to a specific part of the product. WHAT YOU'LL DO As an AI/ML Software Engineer, you’ll play a key role in redefining clinicians’ workflows, unlocking better care for patients, and ensuring Nabla remains at the forefront of AI innovation. You will take ownership of high-impact projects and contribute to the growth and maturity of our engineering team and product. Within a team of experts who have built large-scale production systems at Meta, Google, and Microsoft, you will: - Build agentic pipelines: chains of LLM-based agents that work together on complex clinical documentation tasks. - Go beyond ambient scribing to fully redefine clinicians’ workflows with AI, unlocking transformative efficiencies and improved patient care. - Integrate LLM provider APIs (OpenAI, Anthropic, Google and others) into production features. - Design prompts and build evaluation frameworks to measure and improve output quality, then iterate quickly based on results. - Write clean, production-grade code and own your features end to end, from design to deployment and monitoring in a live healthcare setting. - Work closely with product managers to turn model capabilities into reliable, scalable product features. To get a sense of the impact you’ll have: - Nabla processes 300 billion tokens/month with LLMs. - By the time you’ve read this posting, Nabla will have transcribed 100 new hours of live audio and generated 500 new clinical notes, saving 125 hours of doctor time. YOUR DNA: The ideal candidate will bring strong software engineering skills and a practical, hands-on approach to building with LLMs. - You have 3+ years writing production code, with solid skills in software design, testing, and system architecture. - You’re proficient in one or more widely used back-end languages (Java, Kotlin, C++, Go, etc.). - You’ve built features or products using LLM provider APIs, including prompting and evaluation. You don’t need to have trained or fine-tuned models yourself. - You can turn a fuzzy product requirement into a working, testable pipeline. - You have a passion for going beyond the status quo to redefine workflows and unlock better care for patients. - You communicate clearly and effectively. - Bonus: Experience building multi-agent systems or agent orchestration frameworks is a strong plus. OUR STACK - Kotlin for backend services - LLM provider APIs (OpenAI, Google, Anthropic, and others) for the models themselves - Agent orchestration frameworks for building chains of agents - Prompt evaluation and testing tools to track output quality over time - Google Cloud & Kubernetes for deployment WHERE YOU'LL BE BASED Our offices are based in Paris 3e (Arts & Métiers). Remote policy: 1-2 days a week (with some flexibility). Possibility of working remotely 1-2 weeks once a year. BENEFITS Just like we’re dedicated to supporting doctors’ well-being, ensuring yours is a top priority. We firmly believe that by prioritizing your well-being, we support you to excel in your work. Here are the benefits you get when joining Nabla: - Stock ownership - 100% healthcare coverage - Meal vouchers - Public transportation costs covered at 50% - Exercise class during the workday: Yoga, running, pilates, HIIT - Unlimited budget for book purchases, so you can continue to learn about ML / Healthcare - Culture of trust & accountability — your output matters more than your clock-in time LIFE AT NABLA When you become a part of our company, you join a team of excellence-driven, curious, and genuinely kind individuals. Together, we're committed to making clinicians' lives easier and improving healthcare experiences for everyone. We believe in a world where clinicians can focus on what they were trained to do - caring for their patients, and where no patient feels their visit was rushed. We come to work excited to leverage AI to do more for clinicians. We’re obsessed with our users’ satisfaction and we actively seek out opportunities to engage one-on-one with clinicians to understand how Nabla can better help. We consistently look for ways to improve and do not shy away from doing the work to excel. Whether it’s a feature our users asked for, or a new article for our blog, we prioritize collaboration to deliver exceptional outcomes. We love having fun as much as we love work. Our #nablabla channel is as active as our #feature-show-off channel, we exercise during the work day at least 3 times a week (yoga, running, pilates, or HIIT, your choice!), enjoy regular off-sites to gather the team, and travel to see each other in places like NY, Paris, San Francisco, and many other vibrant cities. Oh, and we’re constantly snacking on chocolate or nuts! If this sounds like an environment you’ll thrive in, we look forward to reading your application! OUR VALUES AT NABLA Joining Nabla means being part of a team that shares a commitment to excellence, humility, growth, and inclusion. Every day is a new chance to excel We aim for nothing less than the best and are willing to put in the effort and dedication required to exceed standards. We learn from yesterday’s failures and do better every day. Stay humble There’s no place for ego in our team. Our collective success is more important than individual achievements. We see humility as wisdom — keeping focus on the bigger picture. Feedback is a gift We embrace feedback and foster a culture of trust and respect that helps everyone grow. We communicate openly about both achievements and challenges, and we actively involve each other in finding solutions. Committed to diversity We recognize the ongoing challenge of diversity in tech. Our responsibility starts with fostering an inclusive environment where everyone feels empowered to be their authentic selves and do their best work. DIVERSITY & INCLUSION Diversity and inclusivity are fundamental values at Nabla. We embrace individuals from various backgrounds, including race, gender, educational history, sexual orientation, and beyond. As an equal opportunity employer, we actively seek out and welcome applicants from diverse backgrounds, believing that a wide range of perspectives enriches our team and enhances our ability to innovate and thrive. AVOID RECRUITMENT SCAMS: STAY SAFE AND INFORMED There is an active employment scam which is now using Nabla to collect personal information or financial scams. If you’re contacted by a Nabla recruiter, please ensure whomever is contacting you truly represents Nabla and is utilizing a nabla.com http://nabla.com email address. We will never ask for the exchange of any money or credit card details during the recruitment process. Nabla utilizes a hiring platform for all applications; please be aware of any suspicious email activity from people who could be pretending to be recruiters or senior professionals at Nabla. You can find more information following this link https://consumer.ftc.gov/consumer-alerts/2023/05/scammers-are-hijacking-job-ads-heres-how-spot-fakes. Nabla does not accept unsolicited CVs from recruiters or employment agencies in response to the Nabla Careers page or a Nabla social media post. Any unsolicited CVs, including those submitted directly to hiring managers, are deemed to be the property of Nabla.
Data Scientist, Product
Mirage is an AI video company focused on making creation dramatically easier. Our team tackles some of the hardest creative and technical challenges in generative media. We get to rethink how people collaborate with AI that can actually do creative work with them—defining entirely new interaction paradigms for AI-native video tools. We make that possible by building multimodal agents that turn creative direction into edits, rendering engines that composite multiple layers of video and graphics, and generative video models trained from the ground up that create new media when needed. All of this comes together in Captions, our creative workspace for generating, editing, and designing video. The technology behind Captions is now available more broadly: Tesseract gives any AI agent native video-editing capabilities, while developers can integrate our models into their own products through APIs. Explore our work Captions https://apps.apple.com/us/app/captions-ai-edits-your-video/id1541407007 — Our flagship creative workspace Tesseract https://x.com/trymirage/status/2102429594804429138 — Our professional video engine for AI agents Research https://mirage.app/research?utm_source=chatgpt.com — The foundation models we build in-house Updates https://x.com/trymirage — The latest from Mirage TechCrunch https://techcrunch.com/2026/03/24/mirage-raises-75m-to-continue-building-models-for-its-ai-video-editing-app-captions/, Forbes https://www.forbes.com/companies/captions/?list=ai50, Fast Company https://www.fastcompany.com/91270234/video-most-innovative-companies-fast-company-2025-youtube-roku-tubi-vimeo-captions-descript-cour-procreate-synthesia-beeble — Press Our Investors We’re very fortunate to have some the best investors and entrepreneurs backing us, including Index Ventures, Kleiner Perkins, Sequoia Capital, Andreessen Horowitz, General Catalyst, Uncommon Projects, Kevin Systrom, Mike Krieger, Lenny Rachitsky, Antoine Martin, Julie Zhuo, Ben Rubin, Jaren Glover, SVAngel, 20VC, Ludlow Ventures, Chapter One, and more. Please note that most of our roles will require you to be in-person at our NYC HQ (located in Union Square) ABOUT THE ROLE We’re hiring two Product Data Scientists to ground the decisions behind Captions in clear metrics, rigorous analysis, and a deep understanding of how people use the product. One person will embed with our iOS team and the other with our web team, working day to day with Product, Design, Engineering, and Data Engineering. You’ll be present as questions and product ideas take shape, not brought in after launch to explain what happened. You’ll define success, evaluate experiments, investigate user behavior, and turn ambiguous findings into clear recommendations. You’ll then follow them through by communicating the reasoning, building alignment, and making sure decisions result in action. We care about analytical depth, product judgment, and the ability to make a team more rigorous. We work AI-first: Codex and Claude Code are part of the everyday workflow, and you’ll be expected to validate the work they help produce. KEY RESPONSIBILITIES - Embed with either the iOS or web team from planning through launch, defining hypotheses, success criteria, core metrics, and measurement approaches while partnering with Engineering and Data Engineering on instrumentation and data quality. - Lead analysis to uncover patterns in product and user behavior, prioritize high-leverage opportunities, recommend actions, and measure realized impact. - Design and evaluate A/B tests and other product measurements, setting appropriate guardrails and turning results into decisions. - Create executive-ready visualizations, dashboards, experiment readouts, and decision memos that drive alignment on what happened and what the team should do next. - Build machine learning and statistical models to solve core user problems, improve the product experience, and deliver measurable user impact. - Design AI-native tools, systems, and automated workflows that accelerate data science and analytics while enabling self-service capabilities. REQUIRED QUALIFICATIONS - A bachelor’s degree in a behavioral or quantitative field, or equivalent practical experience, with a strong statistical foundation. - 3+ years of experience in product analytics or product data science, acting as the analytics voice within a consumer product team. - High proficiency in SQL and either Python or R, including libraries such as pandas, NumPy, SciPy, or Tidyverse, with strong data-model reasoning and the ability to write and understand analytical code. - Experience applying statistical analysis, experimental design, causal inference, and machine learning to product problems, with the judgment to choose an appropriately simple method and translate the result into a specific, actionable recommendation. - Fluency using AI across exploration, analysis, experimentation, and modeling, including providing relevant context, debugging incorrect outputs, and converting repeatable work into reliable workflows. - Strong written and verbal communication skills, including the ability to explain uncertainty and limitations, persuade technical and nontechnical partners, and lead decisions through to action. NICE TO HAVE - Experience with iOS or web analytics, including instrumentation, release measurement, funnels, or experimentation. - Experience with dbt, cloud data warehouses, and large event datasets. - Experience applying product analytics to a consumer creative product. - Experience building reusable AI-powered data science systems, such as agents, orchestration layers, automated analyses, or evaluation and review workflows. - Strong user empathy, curiosity, and excitement about learning in a fast-moving environment. - A proactive, independent working style: you pursue leads, find the people and context you need, operate through ambiguity, and follow work through from recommendation to action. BENEFITS: - Comprehensive medical, dental, and vision plans - 401K with employer match - Commuter Benefits - Catered lunch multiple days per week - Dinner stipend every night if you're working late and want a bite! - Grubhub subscription - Health & Wellness Perks - Multiple team offsites per year with team events every month - Generous PTO policy Captions provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Please note benefits apply to full time employees only.
Software Engineer, Search Systems - Code Data
ABOUT MERCOR Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices. ABOUT THE ROLE Some of the most valuable work on Mercor's platform is code—the tasks, problems, and solutions that train and evaluate the world's frontier coding models. As a Software Engineer for Code Search & Retrieval, you'll own the architecture and algorithms behind how we search across code tasks: finding similar tasks, routing them to the right models, and turning natural-language questions into precise retrieval. Searching over code is a genuinely hard problem. Identifying similar code tasks—not just similar text—requires retrieval that understands structure, semantics, intent, and difficulty, far beyond what generic embeddings capture. You'll own that end to end: the vision is to search across code tasks at scale, identify and select the correct code-specific models for a given task, and translate NLP questions into search queries that return the right code and tasks. This is a hands-on technical leadership role, not a management role. You'll design and build the retrieval systems that combine dense (code) embeddings and lexical (BM25) signals, and make the tradeoffs that let search stay fast and affordable at scale. Because Mercor operates at the frontier of data and models, you'll also own the hardest part of the problem: continuously evolving embeddings, models, and search quality as newer, state-of-the-art code models arrive—without regressing what already works. As an engineer, your impact extends well beyond your own commits. You'll set the technical direction the rest of the org builds on, mentor and grow the engineers around you, and raise the bar for how we build. WHAT YOU'LL DO - Own the architecture of Mercor's code search and retrieval systems end to end—hybrid retrieval combining dense code embeddings and BM25, candidate generation, ranking, and re-ranking over code tasks. - Solve the hard problem of identifying similar code tasks—retrieval that captures code structure, semantics, intent, and difficulty rather than surface text. - Build the system that identifies and selects the correct code-specific models for a given task, and routes tasks to the right model. - Design natural-language-to-query translation that turns NLP questions into precise search over code and tasks. - Design and operate the indexing pipeline so the task index stays fresh and consistent as new tasks, solutions, and results arrive continuously—balancing incremental updates, full rebuilds, and real-time ingestion. - Make the cost-and-speed tradeoffs that keep search fast and economical at scale: embedding dimensionality and quantization, ANN index choice and parameters, caching, sharding, and serving infrastructure. - Build the systems and evaluation harnesses that let us continuously evolve embeddings, models, and search quality—safely swapping in new code models, re-embedding corpora, and A/B testing relevance as SOTA advances. - Define and drive the long-term technical strategy for code retrieval across the organization, and lead the highest-stakes design reviews. - Establish evaluation metrics, offline/online testing, and quality guardrails so search improvements are measurable and regressions are caught before they ship. - Stay deeply hands-on: prototype critical systems, ship production code, and unblock teams on their hardest retrieval and infrastructure problems. - Mentor and grow engineers—junior and senior—through design reviews, pairing, and clear technical writing, raising the technical bar across the org. - Partner with product, researchers, and engineering leadership on build-vs-buy decisions, platform investments, and technical hiring. WHAT WE'RE LOOKING FOR - 8+ years of professional software engineering experience, including 3+ years operating at a Senior level or above, with a Staff-level track record of org-wide technical impact. - Deep, hands-on expertise building search and retrieval systems: dense-embedding retrieval, lexical scoring (BM25/TF-IDF), hybrid ranking, and re-ranking. - Good to have but not required: Experience with code search or code understanding—retrieval over code, code embeddings, or working with code-specific models—and an appreciation for why matching similar code tasks is harder than matching text. - Strong understanding of the search algorithms and index internals—vector/ANN indices (e.g. HNSW, IVF, product quantization), inverted indices, and engines such as Elasticsearch/OpenSearch, Lucene, FAISS, or vector databases. - A track record of making the right cost-vs-speed tradeoffs: latency budgets, throughput, memory footprint, and infrastructure spend on high-QPS systems. - Familiarity translating natural-language questions into structured search queries (query understanding, semantic parsing, or LLM-assisted query generation). - Excellent systems fundamentals: distributed systems, data modeling, and API design at scale. - Demonstrated technical leadership and mentorship—you've helped junior and senior engineers grow and level up an engineering team. - Genuine excitement for agentic development and new technology, fluency with modern AI dev tools (e.g. Claude Code, Cursor, Copilot), and a deep passion for writing great code. - Excellent communication—able to make complex tradeoffs legible to both engineers and leadership. Strong opinions, loosely held. High ownership, pragmatism, and a bias toward shipping. NICE TO HAVE - Experience training or fine-tuning code embedding models or code-specific LLMs. - Experience with learning-to-rank, semantic search, or recommendation systems in production. - Familiarity with LLM-based retrieval, RAG patterns, and model routing/selection. - Background operating latency-critical services on modern cloud and orchestration infrastructure. WHY MERCOR - Impact: Own the code search systems that decide how well Mercor finds, routes, and evaluates the code tasks training the world's frontier models, at a company scaling faster than almost any in its category. - Ownership: Org-wide technical scope with a direct line to engineering leadership and real authority over technical direction. - Learning: Work alongside world-class engineers, product leaders, and AI researchers building at the frontier of AI. - Growth: Shape the engineering organization itself—its standards, its architecture, and its next generation of technical leaders. BENEFITS - Generous equity grant vested over 4 years - Up to $15K relocation bonus (if moving to the Bay Area) - A $10K housing bonus (if you live within 0.5 miles of our office) - A $1.5K monthly stipend for meals - Free Equinox membership - Health insurance Mercor is an equal opportunity employer. We work in-person five days a week in our San Francisco office.
Software Engineer, Machine Learning
ABOUT MERCOR Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices. ABOUT THE ROLE As a Machine Learning Engineer on the Marketplace team, you will build the models and decision systems that power Mercor's hiring engine. This includes search and ranking, candidate-job matching, marketplace recommendations, personalization, and allocation decisions across a rapidly growing talent network. This is an applied ML role with direct product and revenue impact. You will work on problems shaped by real marketplace constraints: sparse and delayed labels, cold start, noisy feedback, heterogeneous supply and demand, and the need to optimize across speed, quality, and conversion simultaneously. WHAT YOU'LL BUILD - Ranking and matching systems that determine which candidates and opportunities are surfaced - Models for recommendation, personalization, and marketplace optimization - Retrieval, scoring, and decision pipelines operating at global scale - Feedback loops that learn from downstream hiring outcomes, not just top-of-funnel engagement - Real-time and batch inference systems embedded in product-critical workflows EXAMPLE PROBLEMS - Improve candidate-job matching using embeddings, structured attributes, and behavioral signals - Optimize ranking toward long-term hiring outcomes under delayed and incomplete labels - Design models that balance marketplace objectives such as fill rate, quality, speed, and conversion - Build systems for candidate allocation, opportunity routing, and liquidity optimization - Develop evaluation and experimentation frameworks that connect model performance to business results WHAT WE'RE LOOKING FOR - Strong track record of shipping ML systems into production - Experience with ranking, recommendation, search, matching, or marketplace problems - Good judgment on model design, objective functions, evaluation, and tradeoffs - Comfort working across the full applied ML stack: data, features, training, inference, and iteration - Strong engineering fundamentals and a bias toward simple, robust systems WHY THIS ROLE This role sits on a core decision layer of the product. Your work will directly shape how talent is discovered, matched, and hired, and will influence fundamental marketplace outcomes across quality, speed, and revenue. TECH STACK Python, Go, embeddings, fine-tuning, RAG, Kafka, Postgres, Redis, Elasticsearch, Kubernetes, Terraform BENEFITS - Bi-annual performance bonus structure - Generous equity grant vested over 4 years - Up to $15k Relocation bonus - $10K housing bonus (if you live within 0.5 miles of our office) - $1.5K monthly stipend for meals - Free Equinox membership - $200 monthly laundry reimbursement - $200 monthly personal wellness reimbursement - Health, Dental, Vision insurance
Research Engineer – Benchmarking
ABOUT MERCOR Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents. Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices. ABOUT THE ROLE As a Research Engineer at Mercor, you’ll work at the intersection of engineering and applied AI research. You’ll own benchmarking pipelines, evaluation systems, and failure analysis workflows that directly inform how we train and improve frontier language models. Your work will define how we measure tool use, agentic behavior, and real-world reasoning. You’ll design and run evals, build rubrics and scorers, and turn failure analysis into actionable improvements for post-training, RLVR, and data pipelines. WHAT YOU’LL DO - Benchmarking: Design, implement, and maintain benchmarks and metrics for tool use, agentic behavior, and real-world reasoning; ensure benchmarks scale with training and stay aligned with product and research goals. - Evaluation systems: Build and operate LLM evaluation systems end-to-end runs, scoring, dashboards, and reporting, so researchers and applied AI teams can track model performance and compare runs at scale. - Failure analysis: Run systematic failure analysis on model outputs (e.g., wrong tool use, reasoning errors, safety/alignment issues); categorize failure modes, quantify prevalence, and feed findings into reward design, data curation, and benchmark design. - Rubrics and evaluators: Create and refine rubrics, automated evaluators, and scoring frameworks that drive training and evaluation decisions; balance rigor with scalability (human vs. model-as-judge, calibration, agreement). - Data quality and usability: Quantify data usability, quality, and impact on key benchmarks; use evals and failure analysis to guide data generation, augmentation, and curation. - Cross-team collaboration: Work with AI researchers, applied AI teams, and data producers to align evals with training objectives and to prioritize benchmarks and failure analyses that matter most. - Ownership in a fast-paced environment: Operate in a high-iteration research setting with strong ownership of benchmarks, evals, and failure-analysis workflows. WHAT WE’RE LOOKING FOR - Strong applied research background, with focus on model evaluation, benchmarking, and/or failure analysis. - Strong coding skills and hands-on experience with ML models and evaluation code. - Solid grasp of data structures, algorithms, and backend systems. - Comfort with APIs, SQL/NoSQL, and cloud platforms for running and storing eval results. - Ability to reason about model behavior, experimental results, and data quality from evals and failure analyses. - Excitement to work in person in San Francisco five days a week in a high-intensity, high-ownership environment. NICE TO HAVE - Industry experience on a post-training or evaluation/benchmarking team (highest priority). - Publications at top-tier venues (NeurIPS, ICML, ACL), especially in evaluation or benchmarking. - Experience building or running LLM evaluations, benchmarks, or failure-analysis pipelines. - Experience with synthetic data generation, rubric design, or RL-style workflows that use evals for reward shaping. - Work samples or code (e.g., eval frameworks, benchmark suites, failure-analysis reports or tooling) that demonstrate relevant skills. BENEFITS - Bi-annual performance bonus structure - Generous equity grant vested over 4 years - Up to $15k Relocation bonus - $10K housing bonus (if you live within 0.5 miles of our office) - $1.5K monthly stipend for meals - Free Equinox membership - $200 monthly laundry reimbursement - $200 monthly personal wellness reimbursement - Health, Dental, Vision insurance
Member of Technical Staff, Applied Research
Join us and help shape the future of AI by defining the narrative around document understanding. ABOUT THE ROLE We are looking for an AI Research Engineer to join our document understanding team. This role is ideal for someone who sits between applied research and strong engineering. You will work on vision-language models, document processing, data curation, synthetic data generation, benchmarking, training, fine-tuning, and post-training. The goal is simple: make our document AI systems more accurate, faster, and more cost-effective in production. You should be excited by frontier AI work, but equally motivated by practical product impact. This is not a pure research role where ideas stay in papers. You will be expected to prototype quickly, evaluate rigorously, and help turn promising approaches into production systems used by customers. WHAT YOU’LL DO - Develop and train vision-language models for document processing and document understanding. - Build data pipelines for data curation, synthetic data generation, labeling, and benchmark creation. - Evaluate base models and perform post-training or fine-tuning to hit specific performance targets. - Improve model accuracy, latency, and cost-effectiveness across real-world document workflows. - Design and maintain benchmarks to measure extraction quality, layout understanding, OCR performance, reasoning accuracy, and end-to-end system reliability. - Work with messy real-world documents, including PDFs, scanned documents, tables, charts, forms, and multi-page enterprise documents. - Collaborate with engineering to move successful research prototypes into production. - Work directly with customers when needed to translate product requirements into benchmarks, experiments, and model improvements. - Stay close to the latest research in vision-language models, document AI, post-training, synthetic data, and agentic systems. - Use modern AI coding workflows and tools to move quickly. WHAT WE’RE LOOKING FOR - 3–7 years of experience in machine learning engineering, applied research, or research engineering. - Strong ML foundation, including hands-on experience benchmarking and training models. - Strong Python skills and comfort with modern ML tooling, especially PyTorch. - Experience with computer vision, vision-language models, NLP, document AI, OCR, extraction, or agentic AI systems. - Ability to build experiments, evaluate results, and iterate quickly toward measurable performance improvements. - Strong engineering judgment and ability to write clean, production-quality code. - Comfort working in a fast-paced startup environment with high ownership and limited structure. - Adaptable, scrappy, and self-directed — someone who can figure things out without waiting to be told. - Strong technical writing and communication skills. NICE TO HAVE - Prior startup experience, especially at an early-stage or high-growth AI company. - Experience as a founder or early startup engineer. - Experience building or improving document processing systems. - Experience with synthetic data generation, post-training, fine-tuning, or benchmark design. - Familiarity with tools such as vLLM, Pydantic, uv, ruff, mypy, Claude Code, Cursor, or similar modern AI engineering workflows. - Experience with open-source AI infrastructure or developer tools. WHO YOU’LL WORK WITH You will work closely with the CTO and the document understanding team, partnering across research, engineering, product, and customer-facing teams. WHY JOIN LLAMAINDEX - Work on a core AI infrastructure problem: making complex documents understandable and actionable for AI systems. - Build production systems at the frontier of vision-language models and document AI. - Join a fast-growing startup with strong open-source adoption and commercial traction. - Work directly with technical founders and a highly ambitious engineering team. - Have real ownership over model quality, product capability, and technical direction. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. LlamaIndex does not accept unsolicited agency resumes. Please do not forward resumes to our jobs alias, employees, or any other organization location. LlamaIndex is not responsible for any fees related to unsolicited resumes.
AI Content Engineer
Join us and help shape the future of AI by defining the narrative around document understanding. ABOUT THE ROLE We are seeking a highly technical ML engineer who can produce compelling, authentic technical content at high velocity. You will combine deep expertise in document AI with strong writing skills to build benchmarks, publish technical analyses, and establish our position as the definitive leader in document understanding. This is not a traditional DevRel or Marketing role. You will write real code, build real benchmarks, and run real experiments - then translate that work into published content at a pace far faster than academic publishing. Your output will directly drive awareness and adoption among the developers building the next generation of document-powered applications. RESPONSIBILITIES - Design, build, and maintain comprehensive benchmarks for document parsing and understanding - Publish high-quality technical content at a weekly cadence (blog posts, benchmark reports, technical comparisons, tutorials) - Stay deeply current with the document AI landscape - new models, papers, competitors, techniques - Run experiments and translate findings into publishable artifacts quickly - Produce technical analyses that demonstrate our capabilities against alternatives - Contribute to open-source examples, notebooks, and documentation - Collaborate with the core ML team to surface improvements and capabilities worth highlighting - Engage authentically with the developer community through technical content (not conferences/events) REQUIRED QUALIFICATIONS - Experience in software engineering (ML engineering + research a bonus) - Strong software engineering fundamentals with production Python experience - Understanding of modern ML techniques, particularly in computer vision, NLP, or multimodal learning - Demonstrated ability to write clearly, quickly, and authentically about technical topics - Bias toward shipping - comfortable publishing at blog pace, not paper pace - Ability to read, understand, and synthesize research papers rapidly - Scrappy and self-directed - can identify what's worth writing about and execute end-to-end - Track record of high-velocity output in fast-paced environments PREFERRED QUALIFICATIONS - Experience with vision-language models, transformer architectures, or document AI specifically - Existing portfolio of technical writing (blog posts, tutorials, technical documentation) - Experience building evaluation frameworks or benchmarks - Familiarity with OCR, layout analysis, table extraction, or document structure understanding - Active presence in ML/AI technical communities - Experience with LLM applications and RAG systems LOCATION In-person in San Francisco. WHY JOIN US? Shape the Narrative: Your content will define how developers think about document understanding. You'll have direct influence on market perception. Technical Credibility: Work with cutting-edge document AI systems processing millions of documents. Your benchmarks and analyses will be grounded in real capabilities. High Autonomy: Significant freedom to identify what matters and publish quickly. No lengthy approval chains. Growth Opportunity: Help build this function from the ground up as we scale. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. LlamaIndex does not accept unsolicited agency resumes. Please do not forward resumes to our jobs alias, employees, or any other organization location. LlamaIndex is not responsible for any fees related to unsolicited resumes.
Senior ML Scientist, AI for Protein Engineering
Your Impact at LILA Lila is building a platform where AI and automation co-evolve to solve the hardest problems in medicine. Within Life Sciences AI, the AI for Protein Engineering team develops and applies generative and predictive models that move biomolecule design programs from in silico hypothesis to wet-lab validated leads. We are looking for a senior individual contributor focused on computational biologics design. The work spans active protein engineering programs and new capabilities that improve how Lila designs, evaluates, and learns from biomolecular sequence, structure, and function data. This role sits at the intersection of machine learning, protein engineering, and therapeutic design. The ideal candidate brings deep ML judgment, intuition for protein biology, and experience delivering computationally-designed, wet-lab-validated biologics through AI. You’ll collaborate with experimental scientists, AI researchers, and platform teams to connect specialist protein design models into Lila’s broader autonomous science platform. What You'll Be Building • Own applied ML workflows for protein engineering campaigns, from design specification through experimental learning. • Develop and adapt methods spanning de novo generation, sequence- or structure-based property prediction, candidate selection, and active learning. Integrate these methods into robust software systems and broader reasoning models. • Translate therapeutic and biological questions into well-defined ML problems, model outputs, and evaluation plans. • Partner with experimental scientists to interpret why designed biomolecules succeed or fail, then turn those insights into better models and design principles. • Build rigorous evaluation frameworks for model generalization to challenging biologics design problems. What You'll Need to Succeed • PhD in Computational Biology, Computer Science, Machine Learning, Biophysics, Bioengineering, or a related quantitative field. • Strong track record applying machine learning to protein design, biologics engineering, or related biomolecular design problems, with industry experience strongly preferred. • Deep ML expertise, with hands-on experience adapting and developing modern AI methods rather than only applying them off the shelf. • Strong intuition for therapeutic biologics design, including sequence, structure, function, developability, and experimental validation considerations. • Demonstrated ability to drive applied research independently, from problem definition through experimental validation and iteration. • Strong collaboration and communication skills across ML, biology, experimental science, and software teams. Bonus Points For • Direct experience designing antibodies, nanobodies, enzymes, peptides, or other therapeutic proteins for applied or clinical pipelines. • Experience with structure prediction, generative protein design, diffusion models, flow matching, or protein language models in a production research setting. • Familiarity with structural biology, conformational dynamics, developability, affinity maturation, or other biophysical constraints. • Experience closing design-test-learn loops with wet-lab teams, including experimental prioritization, high-throughput validation, and active learning. • Publications, open-source contributions, or applied research outputs in AI for science venues. Compensation We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact. U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program. International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market. Expected Base Salary Range $268,000 - $358,000 USD About LILA Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai. Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply. We’re All In Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy . A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
Senior Machine Learning Engineer, Physical Science...
Your Impact at LILA This Machine Learning Engineer for the Physical Sciences team focuses on building and operating end-to-end, scalable machine learning workflows that solve a diversity scientific use cases in materials, chemistry and physical sciences. Your work will advance research efforts on state-of-the-art algorithms to build towards scientific superintelligence across today’s greatest challenges in physical sciences. What You'll Be Building - Design, implement, and maintain end‑to‑end ML pipelines (data ingestion, feature engineering, training, evaluation, deployment, monitoring). - Productionize models and services with robust testing, observability, and documentation in collaboration with cross-functional software teams and build CI/CD workflows and automated evaluations to ensure safe, frequent releases. - Collaborate with domain scientists and platform engineers to translate research insights into performant, scalable systems. - Contribute to technical design reviews, coding standards, and mentoring of best practices. What You’ll Need to Succeed - BS/MS/PhD in Computer Science, Engineering, or a related quantitative field, or equivalent industry experience. - Strong Python software engineering fundamentals (testing, packaging, typing); experience with machine learning frameworks (e.g., PyTorch, Huggingface, etc.). - Experience deploying ML services to production in cloud-based infrastructure (FastAPI/GRPC, containers, orchestration, cloud infra). - Hands‑on experience with model deployment in production systems (LLMs, multimodal models, databases, RAG) with strong debugging and profiling skills. - Clear communication and collaboration in cross‑functional settings. Bonus Points For - Exposure to scientific or engineering domains (materials, chemistry, physics) and related data formats/benchmarks. - GPU optimization experience (CUDA, Triton, compilation, distributed training). - Prior contributions to open‑source ML or scientific software. - Experience with workflow orchestration, data provenance, or large‑scale compute environments. Compensation We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact. U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program. International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market. Expected Base Salary Range $148,000 - $240,000 USD About LILA Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai. Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply. We’re All In Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy . A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.
Machine Learning Sales Engineer(Customer facing)
Who We Are Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction. Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in. We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute. The Way We Work The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice: - Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping. - Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through. - Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together. - Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work. - Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most. - Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact. Machie Learning System Engineer Team: Solutions Engineering Location: [NYC] - Hybrid Reports to: Head of Solution Engineering About Lightning AI Lightning AI is building the platform and infrastructure that make it dramatically easier for teams to develop, train, deploy, and scale AI systems. As the company behind PyTorch Lightning, and following our merger with Voltage Park, we bring together developer-first AI tooling with high-performance compute infrastructure for researchers, startups, and enterprises building at the frontier of AI. We operate with a simple set of values: Move Fast, Focus, Balance, Craftsmanship, and Minimal. We care about people who can operate with urgency, solve hard problems directly, and build with high standards without creating unnecessary complexity. What You'll Do As a customer-facing Machine Learning Solutions Engineer, you will serve as a key technical partner to our go-to-market organization, translating complex customer requirements into scalable AI and machine learning solutions on the Lightning AI platform. You will lead technical discovery, product demonstrations, proof-of-concepts, and post-sales engagements, helping customers successfully evaluate, deploy, and expand their use of the platform. Working at the intersection of customers, sales, product, and engineering, you will bring deep technical expertise to the sales process while translating real-world customer needs into actionable product insights. This highly cross-functional role is critical to accelerating customer adoption, influencing product direction, and demonstrating the business and technical value of Lightning A I. Qualifications - 5-7 years in solutions engineering, sales engineering, field engineering, technical pre-sales, ML infrastructure, or related technical leadership roles. - Deep understanding of complex technical selling, enterprise evaluation cycles, POCs, executive alignment, and cross-functional deal execution. - Credibility with technical buyers and the ability to simplify AI, infrastructure, platform, and architecture concepts for senior business stakeholders. - Experience partnering closely with Sales, Product, Engineering, Customer Success, Partnerships, Marketing, and Revenue Operations. - Strong talent judgment and a track record of hiring, coaching, and developing technical field leaders. - A high-accountability operating style with strong forecasting discipline, field inspection, and customer follow-through. - Comfort leading in a fast-moving startup where the market, product, and GTM motion are evolving quickly. Nice-to-Haves - Experience in AI infrastructure, cloud infrastructure, developer platforms, MLOps, data platforms, GPUs, or high-performance compute. - Experience building a solutions engineering organization through rapid growth or category creation. - Hands-on technical background in software engineering, ML engineering, platform engineering, infrastructure, or data systems. - Experience supporting both direct enterprise sales and partner/channel technical motions. Why Lightning AI - You will help define how companies build and scale real AI systems, not just sell another generic software product. - You will work with technical customers and teammates who care about speed, clarity, craft, and direct problem-solving. - You will join a company with developer credibility, infrastructure ambition, and a GTM motion that is still early enough to shape. We are committed to offering competitive compensation that reflects the value each team member brings to our mission. Final offers are based on factors such as experience, skills, geographic location, and role expectations. In addition to base salary, our total rewards package for eligible roles includes a discretionary bonus, a meaningful equity component, and comprehensive benefits. The anticipated annual base salary range for this role is: $160,000 - $180,000 USD Benefits and Perks We offer a comprehensive and competitive benefits package designed to support our employees’ health, well-being, and long-term success: - Comprehensive Health Coverage: Medical, dental, and vision coverage for employees and eligible dependents. - Meaningful Equity: RSUs that give employees a stake in the company's long-term success. - Retirement Savings: 401(k) matching (U.S.) and pension contributions (U.K.). - Flexible Time Off: Unlimited PTO, company holidays, and floating holidays to support work-life balance. - Company-Wide Winter Break: Two weeks of company closure each winter to disconnect and recharge. - Paid Parental & Family Leave: Paid leave to support you and your family through life's important moments. - Professional Development: Annual learning and development allowance to support your professional growth. - Wellness Benefits: Wellness and work-from-home stipends to support your physical and mental well-being. - Sabbatical Program: Four weeks of paid sabbatical leave after four years of service. - Flexible Work: Flexible schedules and a hybrid work model for our office-based teams. - In-Office Meals: Complimentary meals at our office hubs. Benefits may vary by location, team, and role. At Lightning AI, we are committed to fostering an inclusive and diverse workplace. We believe that diverse teams drive innovation and create better products. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other protected characteristic. We are dedicated to building a culture where everyone can thrive and contribute to their fullest potential.
Principal Machine Learning Infrastructure Research...
Lightmatter is leading the revolution in AI data center infrastructure, enabling the next giant leaps in human progress. The company invented the world’s first 3D-stacked photonics engine, Passage™, capable of connecting thousands to millions of processors at the speed of light in extreme-scale data centers for the most advanced AI and HPC workloads. Lightmatter raised $400 million in its Series D round, reaching a valuation of $4.4 billion. We will continue to accelerate the development of data center photonics and grow every department at Lightmatter! If you're passionate about tackling complex challenges, making an impact, and being an expert in your craft, join our team of brilliant scientists, engineers, and accomplished industry leaders. Lightmatter is (re)inventing the future of computing with light! Lightmatter is seeking a Principal of Machine Learning Infrastructure Researcher to join our Machine Learning team. This team is focused on inventing novel hardware systems using Lightmatter technology and modeling the performance of Lightmatter-enabled systems on both training and inference workloads. This is primarily a creative role where you need to think outside the box to push the boundaries of systems research with the power of silicon photonics at your fingertips. This role will work closely with our product, solutions architecture, and engineering teams to generate impactful performance analysis, create high-performance rack-scale designs, and publish cutting-edge ML systems research. Responsibilities: - Stay up to date on the latest advancements in Machine Learning research. - Build tools to model the performance of Passage-enabled systems when running both training and inference workloads for the latest AI models. - Collaborate with our product and solutions architecture teams to design compelling reference platforms for our customers, powered by Lightmatter technology. - Function as the Machine Learning expert in the room. - Publish academic papers at top industry conferences and journals. Help support the publication of whitepapers, blog posts, and other marketing collateral. - Lead a small team of Machine Learning engineers Qualifications: - PhD in Computer Science, Electrical Engineering, or a related field and at least 10 years of industry or research experience in the field. - Solid background in Machine Learning systems research and experience with performance evaluation and performance modeling. - Hands-on experience with machine learning / deep learning methods and extensive knowledge of Large Language Model training and inference frameworks. - Academic publications in relevant journals and industry conferences. - Strong knowledge of AI systems, including compute (GPUs, TPUs, etc.) and networking (Ethernet, Infiniband, NVLink, etc.) technologies, and their various topologies, paradigms, and trade-offs . - Proven track record leading a team to deliver published results. Preferred Qualifications: - Demonstrated ability to creatively problem-solve and apply first-principles thinking to generate novel ideas, particularly in ambiguous, evolving environments. - Exceptional skills in communicating complex research outcomes and technical concepts to partners and internal stakeholders, while embodying our value of speaking truth. - Ability to work across research, product, and business functions, rolling up your sleeves to bridge gaps and accelerate outcomes, while demonstrating the core value of "We are one Lightmatter." - Adaptable and versatile, thriving in a high-speed, high-precision environment. - Experience in managing engineers and leading projects, with the ability to lift up the team, and work with high standards and high collaboration. We offer competitive compensation. On target earnings range is listed below. The final compensation will be determined based on location, experience, educational background, and market data. Sales Compensation $300,000 - $350,000 USD Benefits - Comprehensive Health Care Plan (Medical, Dental & Vision) - Retirement Savings Matching Program - Life Insurance (Basic, Voluntary & AD&D) - Generous Time Off (Vacation, Sick & Public Holidays) - Paid Family Leave - Short Term & Long Term Disability - Training & Development - Commuter Benefits - Flexible, hybrid workplace model - Equity grants (applicable to full-time employees) Benefits eligibility may vary depending on your employment status and location. Lightmatter recruits, employs, trains, compensates, and promotes regardless of race, religion, color, national origin, sex, disability, age, veteran status, and other protected status as required by applicable law. Export Control Candidates should have capacity to comply with the federally mandated requirements of U.S. export control laws.
Machine Learning Systems Researcher
Lightmatter is leading the revolution in AI data center infrastructure, enabling the next giant leaps in human progress. The company invented the world’s first 3D-stacked photonics engine, Passage™, capable of connecting thousands to millions of processors at the speed of light in extreme-scale data centers for the most advanced AI and HPC workloads. Lightmatter raised $400 million in its Series D round, reaching a valuation of $4.4 billion. We will continue to accelerate the development of data center photonics and grow every department at Lightmatter! If you're passionate about tackling complex challenges, making an impact, and being an expert in your craft, join our team of brilliant scientists, engineers, and accomplished industry leaders. Lightmatter is (re)inventing the future of computing with light! Lightmatter is seeking a Machine Learning Systems Researcher to join our Machine Learning team. This team is focused on inventing novel hardware systems using Lightmatter technology, and modeling the performance of Lightmatter-enabled systems on both training and inference workloads. This is primarily a creative role where you need to think outside the box to push the boundaries of systems research with the power of silicon photonics at your fingertips. This role will work closely with our product, solutions architecture, and engineering teams to generate impactful performance analysis, create high-performance rack-scale designs, and publish cutting-edge ML systems research. Responsibilities - Stay up to date on the latest advancements in Machine Learning research. - Build tools to model the performance of Passage-enabled systems when running both training and inference workloads for the latest AI models. - Collaborate with our product and solutions architecture teams to design compelling reference platforms for our customers, powered by Lightmatter technology. - Function as the Machine Learning expert in the room. - Publish academic papers at top industry conferences and journals. Help support the publication of whitepapers, blog posts, and other marketing collateral. Qualifications - PhD in Computer Science, Electrical Engineering, or a related field. - 2 - 5 years of industry and/or research experience in the field. - Solid background in Machine Learning systems research and experience with performance evaluation and performance modeling. - Hands-on experience with machine learning / deep learning methods and extensive knowledge of Large Language Model training and inference frameworks. - Academic publications in relevant journals and industry conferences. - Must have knowledge of AI systems, including compute (GPUs, TPUs, etc.) and networking (Ethernet, Infiniband, NVLink, etc.) technologies, and their various topologies, paradigms, and trade-offs. - Ability to independently own projects from conception to completion. Preferred Qualifications - Demonstrated ability to creatively problem-solve and apply first-principles thinking to generate novel ideas, particularly in ambiguous, evolving environments. - Exceptional skills in communicating complex research outcomes and technical concepts to partners and internal stakeholders, while embodying our value of speaking truth. - Ability to work across research, product, and business functions, rolling up your sleeves to bridge gaps and accelerate outcomes, while demonstrating the core value of "we are one Lightmatter." - Adaptable and versatile, thriving in a high-speed, high-precision environment. We offer competitive compensation. The base salary range for this role determined based on location, experience, educational background, and market data. Salary Range: total compensation goes beyond base salary, it also includes a new hire equity grant, annual performance-based equity, and other rewards that recognize your impact and contribution. $186,000 - $240,000 USD Benefits - Comprehensive Health Care Plan (Medical, Dental & Vision) - Retirement Savings Matching Program - Life Insurance (Basic, Voluntary & AD&D) - Generous Time Off (Vacation, Sick & Public Holidays) - Paid Family Leave - Short Term & Long Term Disability - Training & Development - Commuter Benefits - Flexible, hybrid workplace model - Equity grants (applicable to full-time employees) Benefits eligibility may vary depending on your employment status and location. Lightmatter recruits, employs, trains, compensates, and promotes regardless of race, religion, color, national origin, sex, disability, age, veteran status, and other protected status as required by applicable law. Export Control Candidates should have capacity to comply with the federally mandated requirements of U.S. export control laws.
Member of Technical Staff, Applied AI
Member of Technical Staff, Applied AI THE OPPORTUNITY We are looking for a Member of Technical Staff with deep expertise in generative modelling to work at the interface between our frontier models and the customers who depend on them. You will join an interdisciplinary team of machine learners, protein engineers and biologists, jointly working to change the way that we control biology and cure diseases. In your role you will develop an in-depth understanding of our proprietary generative models and apply that knowledge to deploy, adapt and optimise them within customer environments - particularly in the pharmaceutical and biotech sectors. This is a hybrid role. You will need a researcher’s depth of understanding of our models, combined with the pragmatism and communication skills to translate that understanding into production systems that deliver scientific value for our partners. WHO WE ARE At Latent Labs, we are building frontier models that learn the fundamentals of biology. We pursue ambitious goals with curiosity and are committed to scientific excellence. Before building Latent Labs, our team co-developed DeepMind’s Nobel-prize winning AlphaFold, invented latent diffusion, and built pioneering lab data management systems as well as high throughput protein screening platforms. At Latent Labs you will be working with some of the brightest minds in generative AI and biology. Our team is committed to interdisciplinary exchange, continuous learning and collaboration. Team offsites help us foster a culture of trust across our London and San Francisco sites. We’re looking for innovators passionate about tackling complex challenges and maximizing positive global impact. Join us on our moonshot mission. WHO YOU ARE - You are a strong ML researcher with experience in generative modelling. You have worked on notable machine learning projects, as documented by your contributions to widely used open source libraries, significant product launches or high impact publications, e.g. at NeurIPS, ICML, ICLR or Nature venues. You have a deep understanding of generative model architectures, training dynamics and inference behaviour. - You are a skilful ML developer. You write ML code that is robust, tested and easy to maintain. You have experience using version control and code review systems. You are a fast prototyper and hacker who can also write beautiful production code. You have experience building systems that serve large models via APIs and running inference on cloud hardware, parallelising data and models across accelerators. - You are customer-facing and delivery-oriented. You thrive in environments where customer success is the primary measure of your work. You can translate complex technical concepts into clear language for scientific and non-technical stakeholders alike. - You are passionate about model performance. You have an detailed understanding of how ML libraries interplay with hardware and data and love to optimise deep learning models for training and inference speed. You use this knowledge to ensure that customer deployments are performant, cost-effective and reliable. - You are mission driven and curious. You are passionate about making a positive impact on the world, whether it’s for patients, customers or beyond. You are motivated by the end goal and are flexible in adapting to different approaches and methodologies. You are curious about problems, however small or big they appear. You thrive in a dynamic environment where you must context-switch between deep technical work and customer-facing engagements. WHAT SETS YOU APART (PREFERRED, NOT REQUIRED) - You have experience in computational biology or protein design. You have worked on ML-driven projects in biology and understand the unique data challenges, evaluation paradigms and scientific workflows of biological modelling. - You have built production enterprise software. You have experience delivering software that meets enterprise-grade requirements for security, compliance, auditability and uptime. - You have a natural science background. You are academically trained in physics, biology, chemistry or other related fields, giving you an intuitive understanding of the scientific problems our customers are solving. YOUR RESPONSIBILITIES Develop, deploy and adapt our models for customer environments: - Develop a deep working understanding of our generative models - their architectures, training data, capabilities and limitations. - Collaborate in a joint codebase with other research scientists, engineers and protein designers, maintaining highest code standards. - Drive the end-to-end technical deployment of Latent Labs models into customer environments, designing production-grade API integrations and model-serving infrastructure. - Adapt and fine-tune models to meet specific customer requirements, collaborating closely with our research team to ensure scientific rigour. - Build ML data pipelines for customer-specific inference, evaluation and feedback workflows. - Ensure deployments meet customer standards for security, performance and reliability. Customer partnership & product feedback: - Work embedded with pharmaceutical and biotech partners to scope technical requirements, troubleshoot issues and deliver solutions. - Serve as the technical point of contact for assigned customers, building trusted relationships with their scientific and engineering teams. - In collaboration with customer biology teams, plan and carry out model inference against biological targets. Quickly learn from results and feed insights back to our models. - Gather and synthesise customer feedback, translating it into actionable insights for our product, research and platform teams. - Create technical documentation, integration guides and best-practice resources. - Spend time working on-site at international partner locations as needed. Self development: - Stay on top of the latest developments in ML, model serving and cloud-native tooling. - Gain a strong working understanding of protein and cell biology. - Collaborate in a joint codebase with other research scientists, engineers and protein designers, maintaining highest code standards. - Participate in knowledge sharing, e.g. organise and present at our internal reading group. - Attend and present at conferences. APPLY We offer strongly competitive compensation and benefits packages, including: - Private health insurance - Pension contributions - Generous leave policies (including gender neutral parental leave) - Hybrid working - Travel opportunities and more We also offer a stimulating work environment, and the opportunity to shape the future of synthetic biology through the application of breakthrough generative models. We welcome applicants from all backgrounds and we are committed to building a team that represents a variety of backgrounds, perspectives, and skills.
Senior Solutions Engineer
ABOUT LANCEDB LanceDB http://lancedb.com/ is a developer-friendly, open-source data lake for multimodal AI. From hyper-scalable vector search to advanced retrieval for RAG, from streaming training data to interactive exploration of large-scale AI datasets, LanceDB is the best foundation for your AI application, and powers some of the most groundbreaking applications and challenging requirements today. ABOUT THE ROLE We are looking for a Senior Solutions Engineer who blends deep technical understanding of AI/ML infrastructure with excellent communication and solution-building skills. In this role, you will serve as a trusted advisor to prospective customers, design partners, and strategic accounts—bridging the gap between cutting-edge AI engineering and real-world business use cases. This role is ideal for someone who thrives at the intersection of technical depth and customer interaction, and who enjoys crafting solutions, demos, and integrations that showcase LanceDB’s strengths in production environments. YOUR RESPONSIBILITIES WILL INCLUDE - Serve as the technical lead in pre-sales conversations—partnering with account executives to scope, solution, and articulate the value of LanceDB for customer-specific workflows. - Lead technical discovery and architecture design sessions with prospects across verticals including AI infra, LLM ops, and multimodal data pipelines. - Build and deliver custom demos and proof-of-concepts to highlight how LanceDB solves challenging RAG, vector search, and feature engineering problems. - Act as the bridge between customer pain points and our engineering/product teams—informing roadmap priorities with real-world feedback. - Partner closely with design partners and early adopters to ensure successful onboarding and expansion. - Champion a superior developer experience with a sharp focus on documentation, SDK ergonomics, and integration workflows. REQUIREMENTS - You thrive in a fast-paced, startup environment and enjoy working with high-caliber teams. - You have 5+ years of experience in a Sales Engineer, Solutions Engineer, ML Engineer, or AI Infrastructure role, supporting AI/ML products or platforms. - Strong knowledge of AI/ML frameworks like PyTorch or TensorFlow, and how they integrate with infrastructure for model training, fine-tuning, and inference. - Hands-on experience working with distributed systems such as Ray, Spark, or Kubernetes. - Familiarity with cloud services (AWS, GCP, Azure) including compute and storage (e.g., EC2, GKE, S3). - Confident communicating with both technical and non-technical stakeholders, and able to translate complex infrastructure into actionable solutions. - You must be based out of the San Francisco Bay Area, and be willing to travel to customer sites as needed. This position is only available to candidates that fulfill this criteria. BONUS POINTS IF YOU - Have experience building or supporting feature engineering workflows or vector search pipelines. - Have worked with feature stores (e.g., Feast, Tecton) or have designed custom ML feature pipelines. - Have experience in observability and monitoring (Prometheus, Grafana, ELK/EFK). - Are familiar with open-source data/streaming frameworks such as Apache Spark, Flink, Delta Lake, Kafka, or Airflow. - Have deep Python skills or are curious about Rust. - Are comfortable creating technical content, workshops, or presenting at meetups/conferences. - Have experience deploying ML infrastructure in customer environments using tools like Terraform, Docker, and CI/CD pipelines. - Have supported enterprise customers or worked in a customer-facing technical capacity before. WHY JOIN US You’ll join a world-class team of open-source builders working on cutting-edge AI infrastructure. You’ll collaborate on systems that power next-generation AI workloads while shaping how LanceDB operates and scales production environments.
AI Research Engineer
ABOUT LANCEDB AI advances at the speed of its research, and research moves at the speed of its data. LanceDB is the AI-native Multimodal Lakehouse: one system where a researcher curates petabytes of video, audio, and every signal derived from them with a few lines of Python, and the next training run starts as fast as the next idea. Customers like Runway, Midjourney, and Netflix build the future of AI on LanceDB, from frontier and world models to robots and autonomous vehicles. ABOUT THE ROLE As the AI research engineer at LanceDB, you'll work with the research team to perform fairly open ended research, focused on end to end training flows across different AI domains, showcasing how LanceDB can be used to accelerate research flows. This is an opportunity to pursue your research interest as an engineer, and have a meaningful impact on raising awareness and significantly improve the product. WHAT YOU'LL DO - Show how Lancedb can be used for training models end to end from curation to modeling across industry verticals - Compare the Lancedb stacked workflow with existing standard training flows with well designed and replicable experiments, that may include benchmarking - Provide core content and work cross-function with to increase awareness for workflow specific features like blobv2, distributed indexing etc. - Publish models and research papers on LanceDB blog platform, social media, and in AI conferences - Partner closely with engineering and product to provide feedback from a researcher’s perspective WHAT WE'RE LOOKING FOR - 5+ years of experience in training deep learning models, not limited to LLM, ideally have worked with video, action, world models before - Proven track record of training SOTA models in an industry vertical - Strong experience in building and maintaining popular OSS repos. - Demonstrated ability to map user feedback from noise to key deliverables - Excellent prioritization skills and demonstrate execution efficiency - Strong sense of product GTM, demonstrate ability to balance strategic thinking with hands-on execution - Passion for staying up-to-date with SOTA AI research and trends NICE TO HAVE - Experience with training transformer based models, and post-training/alignment - Hands on experience with PyTorch, distributed training, and tensor parallelism - 5+ years of experience, including working at startups
Senior Solutions Architect - Data Labs
About Invisible Invisible Technologies makes AI work. Our end-to-end AI platform structures messy data, automates digital workflows, deploys agentic solutions, measures outcomes, and integrates human expertise where it matters most. Our platform cleans, labels, and structures company data so it is ready for AI. It adapts models to each business and adds human expertise when needed, the same approach we have used to improve models for more than 80% of the world’s top AI companies, including Microsoft, AWS, and Cohere. Our successes span industries, from supply chain automation for Swiss Gear to AI-enabled naval simulations with SAIC, and validating NBA draft picks for the Charlotte Hornets. Profitable for more than half a decade, Invisible reached $134M in revenue and ranked as the number two fastest growing AI company on the 2024 Inc. 5000. In September 2025, we raised $100M in growth capital to accelerate our mission of making AI actually work in the enterprise and to advance our platform technology. About the Role Invisible's Data Lab works with leading AI companies to design and deliver the human data that advances model capabilities and measures performance. Our work spans the full frontier of AI training and evaluation—from supervised fine-tuning and preference data to benchmarks, red teaming, multimodal data, coding tasks, and agent environments. Solutions architects are at the center of this work, working with clients to define the problem and translating open-ended model development problems into scalable programs with sound methodology. We are looking for a Senior Solutions Architect to bring technical depth, sharp commercial judgment, and a strong command of the rapidly evolving AI training and evaluation landscape. Embedded within an account team working with a major frontier AI lab, you will lead solution design for new opportunities and pilots. You will partner with Project Leads who own execution, client management, and overall project outcomes, while remaining accountable for the integrity, feasibility, and scalability of the solution through handoff and early delivery. You will often enter when a client's ask is still underspecified. Your job is to understand what they are really trying to achieve, translate that into a collection we can execute, and help the client and our teams make the right technical, methodological, and commercial choices. You will connect the perspectives of client researchers, program managers, Project Leads, experts, and engineers—bringing context that may not otherwise exist in the room. This is not a traditional infrastructure architecture role, nor is it primarily a project management role. It sits at the intersection of ML research, human data, product thinking, and client partnership. Success means teams make better decisions because you are involved: projects are framed correctly, solutions reflect the state of the art, and clients trust Invisible to understand where their work is going next. You will work across a remarkable range of frontier AI problems, with direct exposure to the researchers and program leaders defining them. You will help determine how novel human-data programs should work before there is an established playbook, influence the products and systems used to deliver them, and build reusable ways of thinking that shape how Invisible approaches the field. If you live and breathe LLM training and evaluation, communicate with unusual clarity, and want your judgment to matter in the room, this role offers a rare combination of technical range, client proximity, and real-world impact. What You'll Do - Equip account teams with the technical and research context they need to fully understand client requests and make sound decisions about how to approach them. - Own the solution design of new human-data projects and pilots, working with Project Leads from early discovery and scoping through the transition into execution. - Work directly with client research and program management stakeholders to interpret ambiguous requests, uncover the underlying objective, and turn it into a clear and executable approach. For technical POCs in particular, you may take the lead in the relationship alongside the Project Lead. - Advise on methodology/best practices across supervised fine-tuning, preference data and RLHF, benchmarks and evaluations, red teaming, multimodal data, coding tasks, agent environments, and emerging approaches. - Shape task design, annotation and evaluation workflows, quality and validation strategies, data structures, and the trade-offs between research value, feasibility, speed, scale, and expert experience. - Define the expert profiles a project requires, including the knowledge, experience, and judgment needed to produce credible data. - Bring commercial judgment to early scoping, building rough-cut estimates of effort, expert supply, timeline, and cost so teams can test feasibility and shape viable proposals. - Partner with Engineering to define the expert-facing interfaces and technical architecture that collections require, including workflows, APIs, data exchange patterns, synthetic testing, and validation mechanisms. - Prototype or test critical elements of a solution during discovery, while working with Engineering as the team responsible for production implementation. - Build alignment across clients, account leadership, Project Leads, Engineering, expert operations, and other internal teams. - Stay deeply current on LLM training, post-training, human data, benchmarks, and evaluation research, and translate new developments into practical implications for active and future client work. - Develop and share reusable mental models, patterns, and emerging best practices that raise the technical fluency and solutioning quality of the wider organization. What We Need Required - Deep, demonstrable interest in how frontier AI models are trained, improved, and evaluated. You actively follow the space and can form your own views rather than simply repeat its vocabulary. - Strong working knowledge of LLM training and evaluation concepts, with the ability to reason across data modalities, collection methods, and research objectives. - Exceptional communication skills. You can listen closely, identify what is missing from an underspecified request, explain complex ideas clearly, and influence technical and non-technical stakeholders. - The judgment to challenge assumptions constructively while maintaining momentum and client trust. You know when to ask another question, when to recommend a different approach, and when to make a pragmatic call. - Enough technical fluency in ML, data systems, software architecture, APIs, and data flows to collaborate credibly with researchers and engineers and to shape implementable solutions. - Commercial judgment and comfort with rough-cut budgeting, particularly when weighing research ambition against expert availability, delivery effort, timeline, cost, and unit economics. - Ability to synthesize client context, research intent, expert capabilities, user experience, technical constraints, and operational realities into an executable approach. - Experience working across multiple stakeholders or teams, particularly in ambiguous environments where responsibilities and requirements continuously evolve. - High ownership, curiosity, adaptability, and low ego. You care more about reaching the right answer and improving the outcome than being the person who supplied it. Nice to Have - A background in computer science, software engineering, machine learning, data science, AI research, technical product, solutions architecture, or a related field. Equivalent knowledge gained through independent work or other professional experience is equally welcome. - Direct experience with human-data programs, data annotation, model post-training, RLHF, RLVR, evaluations, benchmarks, red teaming, or research operations. - Experience partnering directly with AI researchers, research engineers, or technical program managers. - Hands-on ability with tools such as Python, SQL, APIs, notebooks, or lightweight prototyping. You will not be the primary builder, but being able to inspect, test, or demonstrate an idea can make you more effective. - Experience designing tools or workflows for specialized users, particularly where expert judgment and data quality are central to the outcome. - Evidence of serious engagement with the field through professional work, research, open-source contributions, technical writing, independent projects, benchmark participation, or other forms of self-directed learning. You can find more information about our geographic pay tiers here . During the interview process, your Invisible Talent Acquisition Partner will confirm which tier applies to your location. For candidates outside the U.S., compensation is adjusted to reflect local market conditions and cost of living. *Bonuses and equity are included in all full-time offers. Final compensation is determined by a combination of factors, including location, job-related experience, skills, knowledge, internal pay equity, and overall market conditions. Because of this, every offer is unique. Additional details on total compensation and benefits will be discussed during the hiring process. What It's Like to Work at Invisible: At Invisible, we’re not just redefining work—we’re reinventing it. We operate at the intersection of advanced AI and human ingenuity, pushing the boundaries of what’s possible to unlock productivity and scale. Ownership is at the core of everything we do. Here, you won’t just execute tasks—you’ll build, innovate, and shape the future alongside world-class clients pushing the boundaries of AI. We expect bold ideas, relentless drive, and the ability to turn ambiguity into opportunity. The pace is fast, the challenges are big, and the growth is unmatched. We’re not for everyone, and we’re okay with that. If you’re looking for predictable routines, this isn’t the place for you. But if you’re driven to create, thrive in dynamic environments, and want a front-row seat to the AI revolution, you’ll fit right in. Country Hiring Guidelines: Invisible is a hybrid organization with offices and team members located around the world. While some roles may offer remote flexibility, most positions involve in-office collaboration and are tied to specific locations. Any location-based requirements or hybrid expectations will be communicated by our Talent Acquisition team during the recruiting process. AI Interviewing Guidelines: Our hiring team thoughtfully uses AI to support an efficient, engaging, and inclusive interview process. Since AI can also be a helpful tool for candidates, we've outlined expectations for using it ethically throughout your interview journey. Click here to learn more about how we use AI and our guidelines for candidates. Accessibility Statement: We are committed to providing reasonable accommodations for individuals with disabilities. If you require an accommodation to participate in the application or interview process, please submit your request using our accommodation request form. A member of our team will follow up to support your request. Here is the link the google form: https://docs.google.com/forms/d/e/1FAIpQLScqsu0PCQ0m0lwt-VFeXovNbZxWk0jogv8QI_ciGqZsIbSBjQ/viewform?usp=sharing&ouid=102356854798492362557 Equal Opportunity Statement: We’re an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, or veteran status, or any other basis protected by law. Due to a high volume of candidates, Invisible may use automated decision-maker technologies to filter candidates based on response to our application questions and other provided information. Our use of automated decision-making enables us to be efficient by providing a manageable list of possible candidates that meet our mandatory hiring criteria. If you object to our use of automated decision-making please contact us.
Data Scientist, Product
WHY WORK AT HIGGSFIELD AI? Higgsfield AI is the fastest-scaling generative AI company in history, hitting $500M in annual revenue run rate, 25M+ users worldwide, 6M+ generations per day, and powering 390 of Fortune 500 brands. We're building at the absolute frontier of AI-powered video creation and next-generation creative tools. Joining Higgsfield means becoming part of a high-impact team shaping the future of AI-native experiences, at a company that isn't just moving fast, but rewriting what fast looks like. WHAT THIS ROLE MEANS AT HIGGSFIELD Right now we treat 25M+ people as one user. The same paywall, the same discount, the same lifecycle email, the same retention effort. And we mostly conclude that a campaign worked by looking at what the people we sent it to did next — which is not the same thing. This role makes the company act per user, and know what that action was worth. You own both halves of that: the models that decide who we aim at, and the causal reads that say whether it landed. Who is about to leave. Who will come back on their own without us spending anything. Whose decision a discount actually changes. And what a payer is worth over their life, once the discount, the refunds and the compute they consume are all inside the number. You make sure: - Discounts and credit vouchers go to the people whose behaviour they change, and nowhere else - We know who is about to churn early enough to do something about it — and we know whether the something worked - Lifetime value is a number we can defend by cohort, by plan and by the discount a cohort was acquired on, not one blended average that hides all three - Every model and every campaign has a holdout behind it, so we can always state what it is worth in money WHAT YOU WILL DO Churn, retention & lifecycle propensity - Build churn and downgrade prediction for subscribers, and repeat-purchase propensity for one-time credit-pack buyers — two different machines, not one model with a flag on it. - Know the difference between a model that predicts churn and a model that reduces it. AUC earned by detecting users who already stopped using the product is worth nothing. - Hunt leakage relentlessly. Cancellation-adjacent features will hand you a beautiful offline number and a useless system. - Pair every score with an intervention and a holdout. The deliverable is a retained user, not a ranked list. Uplift modelling & offer targeting - Own incrementality on everything we aim at a user: discounts, credit vouchers, trials, upgrade prompts, win-back campaigns, retention saves. - Model uplift, not propensity.Targeting the users most likely to convert spends margin on people who were going to convert anyway; the entire value of this work is finding the users whose decision the offer changes. - Design the randomized holdouts that make uplift trainable and measurable in the first place, and keep them running permanently — including the ones marketing will ask you to switch off. - Respect the guardrails. An offer model optimizing conversion alone will happily find the accounts that convert at negative gross margin, and it will find the abuse rings first. Margin and abuse signals are constraints on the objective, not a later cleanup. - Work with Legal on what may be personalized. Personalized pricing and discounting touches consumer-protection and consent rules in several of our markets — you should want that conversation, not route around it. Lifetime value & payer economics - Own LTV: cohort-based, censored honestly, stated on contribution margin rather than gross revenue, and split by plan, segment and acquisition discount. - Model the parts that actually move it — refunds and chargebacks, credit breakage and expiry, plan migration and downgrades, and the compute an unlimited plan consumes. - Make it a decision input, not a slide: what we can pay for a user, which discount depth pays back, which plan we should stop selling. - Keep it current and versioned. A stale LTV curve is worse than no curve, because people act on it. Experimentation & causal measurement - Design and read the experiments behind pricing, packaging, offers and lifecycle — with the product analytics team, not in parallel to it. - Reach for the right tool when a clean experiment is impossible: difference-in-differences, matching, switchback and geo tests, synthetic control — and name the assumption each one is buying. - Separate "users who do X retain better" from "making users do X improves retention." Every time, out loud. - Kill your own results. A finding that doesn't survive a second look should die at your hands, not in a board deck. How we ship - Own the work end to end: framing the decision, features, training, evaluation, the scored audience landing in the tool that sends the offer, monitoring, retraining. - Models ship as batch scores and policy rules into lifecycle tooling, the paywall and pricing — plus the readout that says what they earned. There is no real-time serving stack to babysit. - Monitor for drift. New model launches, price changes, discount campaigns and seasonality all move the ground under a deployed score. - Write down what each model is worth, in money, and keep that number current. WHO WE'RE LOOKING FOR - Experience shipping models that changed what the business did to real users— a targeted campaign, a pricing rule, a retention programme. Notebooks and offline benchmarks are not this. - Depth in at least two of: churn and propensity modelling, uplift & causal ML, LTV and subscription economics, experimentation at scale. - Genuine causal literacy: randomized holdouts, incrementality, Qini and uplift curves, selection effects, and why a lift measured before-and-after is usually not a lift. - Strong SQL and Python: cohorts, funnels and retention on raw event data without help; gradient boosting; and the boring parts — feature pipelines, retraining cadence, a scoring job that runs on a schedule and doesn't rot. - Understanding of subscription plus one-time purchase mechanics: recurring vs one-off revenue, refunds, plan changes, the deferred value of unspent credits, and why revenue and margin disagree about the same customer. - Commercial judgment: you name the decision and the metric before you pick the model, and you know when the answer is a rule rather than a model. - Pragmatism. A segment rule shipped this month that lifts retention beats a two-quarter uplift platform. Then you replace the rule. - Clear written and spoken English, B2+. Backgrounds that often do well: - Growth, retention or monetization data scientists from subscription, gaming, fintech or e-commerce — anywhere offers and churn are modelled with real money attached - CRM and lifecycle data scientists who owned campaign targetingandits incrementality - Causal inference and uplift specialists who ship policies rather than papers - Strong product analysts who moved into modelling and kept the decision instinct WHAT THIS ROLE IS NOT This role is not a fit if you: - Want to train or fine-tune generative video models — that's what our R&D ML Engineer roles do, and they're open - Want to own a recommender or a serving stack — ranking is not in this role's scope today - Would target an offer at the users most likely to buy and call it personalization - Report a lift from a before-and-after comparison, or from comparing the users you targeted to everyone you didn't - Optimize AUC and hand the score to someone else to work out what to do with it - Need clean labelled data, a feature store and an experimentation platform to exist before you can start - Want predictable 9–5 workdays HIRING PROCESS We move fast: - Screening call (30 min) - Technical interview & case study (1 hour) - Practical home task (7 days) - Team interview (60 min) - Paid on-site trial (1 month) WHAT WE OFFER - Competitive base salary in USD, based on your experience, skills, and the scope of the role. - Equity participation through the company’s stock option program, giving you the opportunity to share in Higgsfield’s long-term growth. - Relocation support to Almaty for candidates moving from another city or country. - A highly collaborative, fast-paced environment where you can work directly with experienced leaders and have a meaningful impact on the product and company. - Opportunities for professional growth, ownership, and career development as the company scales. - Company-provided equipment, meals, transportation, or other office benefits.
AI Research Engineer - Robotics
Who we are Helsing is a defence AI company. Our mission is to protect our democracies. We aim to achieve technological leadership, so that open societies can continue to make sovereign decisions and control their ethical standards. As democracies, we believe we have a special responsibility to be thoughtful about the development and deployment of powerful technologies like AI. We take this responsibility seriously. We are an ambitious and committed team of engineers, AI specialists and customer-facing programme managers. We are looking for mission-driven people to join our European teams – and apply their skills to solve the most complex and impactful problems. We embrace an open and transparent culture that welcomes healthy debates on the use of technology in defence, its benefits, and its ethical implications. The role At Helsing we deliver AI-based capabilities and the enabling foundation that allow machines to perceive and assist human decision-making. You will have the unique opportunity to shape AI capabilities in one of the most challenging sectors, where high generalisation capabilities need to be paired with hardware constraints and real-world robustness. You will be part of a team pushing the boundaries of autonomous robotics through reinforcement learning. Your work will focus on designing, training and deploying RL-based controllers for robots operating in complex, unstructured, and dynamic real-world environments — where classical control approaches fall short. This includes enabling robots to perceive and understand their surroundings by fusing rich sensory inputs, including vision, to inform robust and adaptive control. You will own the full pipeline from simulation to deployment, ensuring that learned policies are robust, efficient, and ready for real-world operation under tight hardware constraints. You should apply if you - Hold an MSc or PhD in Robotics, Machine Learning, Control Engineering, or a closely related field, with a strong focus on reinforcement learning and robot control. - Have hands-on experience training and deploying RL-based controllers on real robotic hardware, not just in simulation — you have seen your policies fall, iterate, and ultimately succeed on a physical system. - Are deeply familiar with modern RL techniques for continuous control, including but not limited to: model-free methods (PPO, SAC, TD3), model-based RL, hierarchical RL, sim-to-real transfer strategies, domain randomisation, and curriculum learning. - Have a solid understanding of robot dynamics, kinematics, and classical control theory (e.g. PID, model predictive control, trajectory optimisation), and know when and how to combine them with learned approaches. - Are proficient in building and working with physics-based simulators (e.g. MuJoCo, Isaac Gym/Isaac Lab, PyBullet, Gazebo) for training and validating RL policies. - Possess solid software engineering skills, writing clean and well-structured code in Python and/or languages like Rust or modern C++, and have experience deploying AI software to production including testing, QA, and monitoring. - Have excellent communication skills and the ability to report and present research findings clearly and efficiently, both internally and externally. - Are passionate about keeping up to date with current research and enjoy re-implementing and extending state-of-the-art papers. Note: We operate at an intersection where women, as well as other minority groups, are systematically under-represented. We encourage you to apply even if you don’t meet all the listed qualifications; ability and impact cannot be summarised in a few bullet points. Nice to have - PhD in Robotics, Reinforcement Learning, Control Engineering, or related fields, with publications in top-tier venues (e.g. CoRL, ICRA, NeurIPS, ICLR, IROS, RSS). - Experience developing controllers for highly dynamic robotic systems operating under complex contact interactions and demanding environmental conditions. - Experience with vision-based perception for robotics control, such as depth estimation, visual odometry, or visuomotor policy learning. - Familiarity with low-level motor control interfaces and real-time embedded systems constraints. - Experience with online adaptation and meta-learning techniques to enable robots to adapt to changing environmental conditions - Experience with sensor fusion (IMU, proprioception, exteroception, vision) to inform and enhance learned control policies. Join Helsing and work with world-leading experts in their fields - Helsing’s work is important. You’ll be directly contributing to the protection of democratic countries while balancing both ethical and geopolitical concerns. - The work is unique. We operate in a domain that has highly unusual technical requirements and constraints, and where robustness, safety, and ethical considerations are vital. You will face unique Engineering and AI challenges that make a meaningful impact in the world. - Our work frequently takes us right up to the state of the art in technical innovation, be it reinforcement learning, distributed systems, generative AI, or deployment infrastructure. The defence industry is entering the most exciting phase of the technological development curve. Advances in our field of world are not incremental: Helsing is part of, and often leading, historic leaps forward. - In our domain, success is a matter of order-of-magnitude improvements and novel capabilities. This means we take bets, aim high, and focus on big opportunities. Despite being a relatively young company, Helsing has already been selected for multiple significant government contracts. - We actively encourage healthy, proactive, and diverse debate internally about what we do and how we choose to do it. Teams and individual engineers are trusted (and encouraged) to practise responsible autonomy and critical thinking, and to focus on outcomes, not conformity. At Helsing you will have a say in how we (and you!) work, the opportunity to engage on what does and doesn’t work, and to take ownership of aspects of our culture that you care deeply about. What we offer - Competitive salary and VSOP options - Relocation support : up to €2,500 and 4 weeks temporary accommodation - Learning : €500/£450 yearly allowance - Health & wellness : gym membership and mental health support (Nilo.health) - Social : regular company events and monthly social allowances - Enhanced parental leave : 22 weeks fully paid for primary caregivers & 6 weeks for secondary caregivers - Family support: 5 days of paid family emergency leave, 100% remote work option during pregnancy and phased return to work These are the core benefits across all locations, there may be additional benefits in certain locations. Helsing is an equal opportunities employer. We are committed to equal employment opportunity regardless of race, religion, sexual orientation, age, marital status, disability or gender identity. Please do not submit personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, trade union membership, data concerning your health, or data concerning your sexual orientation. Helsing's Candidate Privacy and Confidentiality Regime can be found here .
Senior AI Infrastructure Engineer
We’re Heidi. We're building the future of healthcare by giving every clinician the earth's finest AI Care Partner. In just 18 months, our clinical AI products have absorbed the administrative chaos of 73 million patient visits. Today, we support over 2.5 million patient sessions a week across 190+ countries. Healthcare systems are failing us; clinicians spend more time on documentation than on patients, and the human connection that makes medicine worth practicing is eroding. Our mission is simple: double the world’s healthcare capacity and strengthen the human connection at its heart. We found product-market fit with a freemium medical scribe that clinicians love. Now, we're expanding. Every task a clinician hands to Heidi is a patient who feels more attended to, a health system unclogged, and a clinician who gets to be a clinician again. If you don’t choose easy and you want to build something way bigger than yourself then, choose the challenge, choose Heidi. The role This role sits in the model team, the researchers and engineers who train, deploy, and own the AI models behind every Heidi product. You’ll build and operate the infrastructure that makes those models fast, reliable, and cost-effective at scale. Your work will span production model serving, GPU cluster management, and the infrastructure supporting training and evaluation. You’ll decide how workloads share compute, diagnose performance bottlenecks, and build the deployment and observability tools that help the team move quickly with confidence. We’re looking for a hands-on engineer who has deployed and operated models in production, understands the demands of GPU workloads, and can take a system from initial design through rollout, incidents, and ongoing improvement. You’ll partner closely with researchers and our platform engineers, with ownership of the systems you build. What you’ll do - Build and own model-serving infrastructure. Take models from checkpoint to production, with repeatable deployment pipelines, request routing, autoscaling, fallback paths, and controlled rollouts and rollbacks across regions. - Manage GPU clusters and workload scheduling. Improve resource allocation across inference, training, and evaluation. Build scheduling policies around workload priority, quotas, hardware topology, and recovery requirements so online services stay responsive while other workloads make productive use of capacity. - Improve inference performance. Profile real workloads and improve latency, throughput, and memory efficiency. Evaluate batching, KV-cache management, quantization, speculative decoding, and parallelism strategies against production traffic and quality requirements. - Support distributed training and model iteration. Give researchers reliable ways to launch fine-tuning and training jobs, manage model artifacts, save and restore checkpoints, and move validated models into serving. Reduce time lost to failed jobs, slow data loading, and manual setup. - Make deployments observable and incidents traceable. Connect application requests and sessions to the exact model, deployment configuration, and worker that served them. Build dashboards and alerts covering model latency, queueing, errors, GPU health, memory pressure, and workload performance. - Own production reliability. Define service objectives, investigate incidents across the application, inference engine, GPU, and network layers, and build recovery procedures that work. Turn recurring failures into fixes, automated checks, and useful runbooks. - Make compute costs actionable. Track GPU usage, idle capacity, and inference cost by model and workload. Use capacity forecasts and measured performance to guide deployment choices and improve cost per successful request without sacrificing quality or reliability. - Build a platform the model team can use independently. Automate provisioning, configuration, benchmarking, and releases. Partner with the engineers behind ASR, note generation, Evidence, and Dictate so new models can be deployed and evaluated through consistent, well-supported workflows. What you'll need - A strong engineering foundation. Hands-on AI infrastructure experience. At least 1 year building and operating infrastructure for large language models, including model deployment, inference serving, or distributed training. You can design the system, write the code, and own it in production. - Production model deployment experience. You’ve deployed and maintained LLMs or other demanding ML workloads using engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, or comparable systems. You understand the work between loading a model and running a reliable service. - GPU cluster and orchestration experience. You’ve managed GPU workloads using Kubernetes, Slurm, or an equivalent platform, with practical experience in scheduling, resource allocation, capacity planning, and failure recovery. - Performance debugging skills. You can use traces, metrics, and profiling tools to distinguish compute, memory, communication, and scheduling bottlenecks. You understand how batch size, context length, precision, and multi-GPU execution affect performance and cost. - Strong software and systems skills. You’re proficient in Python and comfortable with backend or systems development in Go, C++, Rust, or a comparable language. You have practical experience with Linux, containers, deployment automation, and distributed services. - Operational ownership. You’ve owned production incidents, built useful monitoring, and made releases recoverable. You can explain the trade-offs behind a design and work effectively with researchers, product engineers, and infrastructure partners. Nice to have - Experience with distributed training frameworks such as PyTorch FSDP or Megatron, or infrastructure for reinforcement learning and rollout generation. - Experience tuning inference engines, serving MoE models, or implementing quantization, speculative decoding, and prefill/decode disaggregation. - Familiarity with GPU interconnects, NCCL, RDMA, topology-aware scheduling, or diagnosing multi-node communication problems. - CUDA or Triton kernel development, contributions to AI infrastructure projects, or experience building cluster operators and scheduling integrations. - Experience operating infrastructure across multiple regions or providers, particularly for healthcare or other sensitive production workloads. How we show up - Build for the next decade, not next quarter. Our targets are outrageous on purpose. The world's health doesn't have the luxury of incrementalism. - Lead, don't wait. We treat tomorrow's problems today. Sometimes we build what's needed before it's wanted, and we're fine with that. - Follow the evidence. Trust the patient. We pursue truth relentlessly. But when the subjective and objective disagree, we treat the patient, not the numbers. Ego is a comorbidity we can't afford. - Own the outcome. Everyone here carries the company. Raise problems with solutions, solve them end-to-end, and never be a bystander. - Ship, measure, go again. A button today, a workflow tomorrow. More iterations beat better planning. We're precise at pace, not reckless. - Live in clinicians' reality. Not the ideal workflow, the twenty-patients-before-lunch actual one. We build for exhausted humans, and we'd better be decent ones while we do it. Why Heidi? You’ll join a team focused on real-world impact over imaginary valuations and glossy PR. We live and breathe the challenges of modern health systems, and are laser-focused on exacting the change we’d like to see. We’re medicos, engineers, builders, and designers who’ve felt the moral and practical toll of what non-care feels like. True A-players progress extremely fast here. The nature of the scale-up game is demanding, but we value sustainable performance and mental health. You're trusted to perform, and you set your schedule. We operate on outcomes > inputs, not process theatre. We all take the bins out, metaphorically and literally. Building what we’re building isn’t always easy. But we didn’t choose easy, we chose to build something that actually matters. We hold ourselves to a higher standard because healthcare demands it. If you join Heidi, you recognise that the deeper question isn’t whether AI can solve the global healthcare crisis, but whose hands will shape it. The work is hard, but you will trust and admire the people you work beside, and rest easy knowing you’re doing the defining work of your career. We take care of you. We offer a $1,000 annual learning and development budget, a $150/month health and wellness allowance, a $500 home office budget, 26 weeks paid primary parental leave and 18 weeks paid secondary parental leave, fertility support up to $10,000, four weeks of work from anywhere per year, and serious equity.
Software Engineer: ML Optimization
ABOUT GENERALIST At Generalist, we are on a mission to build general intelligence for the physical world and make it useful to everyone. We believe the industries and homes of the future will depend on humans and machines working together in new ways. Robots can help us build more and get more done. We build embodied foundation models, starting with a focus on dexterity. This requires advancing the frontiers of data, models, and hardware, to enable robots to intelligently interact with the physical world. The company embraces both large-scale AI and robotics as core to its DNA. Our team of researchers, roboticists, and company builders come from OpenAI, Boston Dynamics, Google DeepMind, and other frontier labs—with a track record of shipping AI breakthroughs. Before Generalist, we pioneered large embodied multimodal models and vision-language-action models (PaLM-E, https://research.google/blog/palm-e-an-embodied-multimodal-language-model/ RT-2 https://deepmind.google/blog/rt-2-new-model-translates-vision-and-language-into-action/, Gemini Robotics https://deepmind.google/models/gemini-robotics/), launched and scaled ChatGPT https://chatgpt.com/ and GPT-4 https://openai.com/index/gpt-4-research/ to hundreds of millions of users, engineered the foundations of autonomous driving, built next-generation robots (Atlas https://bostondynamics.com/atlas/, Spot https://bostondynamics.com/products/spot/, Stretch https://bostondynamics.com/products/stretch/) and pushed the limits of what they can do (from parkour https://www.youtube.com/watch?v=tF4DML7FIWk to manipulation https://bostondynamics.com/blog/large-behavior-models-atlas-find-new-footing/, and testing robustness https://www.youtube.com/watch?v=aFuA50H9uek). We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. ABOUT THE ROLE We internally call this team MBMB (More Big More Better). You will own optimizations on both the training and on-robot inference stacks. We are still in a regime of step-function, not incremental, gains. You’ll be responsible for: - Making GPUs go brrrrr - Implementing ML, hardware, and software changes that lead to step-function gains - Optimizing both the inference and training stacks You might thrive in this role if you: - Are proficient and stay current with the latest ML techniques for training and inference optimizations in transformer and diffusion based architectures - Will chase ML optimizations anywhere: From the CUDA kernels, to ML architecture, to frontend or backend network bottlenecks, CPU bottlenecks, NVLink and comms, to torch, numpy, and Python inefficiencies.
Research Engineer
ABOUT THE ROLE As a Research Engineer at Gamma, you'll build models for visual communication, a foundational bet for the company. You'll teach models to reason about spatial composition, hierarchy, and visual language the way a skilled communicator or designer does. This work sits at the intersection of research rigor and product impact. You’ll have the opportunity to build evals and training data for a field where there isn’t much of either. You’ll fine-tune vision-language models so that Gamma's 100M+ users get exceptional design every time they generate. You'll succeed here if you combine deep expertise in VLMs and multimodal modeling with a research mindset, comfort working in ambiguity, and a rigorous eye for visual and design quality. Our team has a strong in-office culture and works in person 4 to 5 days per week in San Francisco. We love working together to stay creative and connected, with flexibility to work from home when focus matters most. WHAT YOU'LL DO - Fine-tune vision-language models to generate and critique layouts, reason about design intent, and translate content into coherent visual form - Design evaluation frameworks and benchmarks for visual communication quality, covering layout, typographic structure, color, and information density, the dimensions generic text evals miss - Lead proprietary data collection for visual design tasks, building the datasets needed to teach models design principles that aren't available off the shelf - Run rigorous experiments to understand model behavior, then turn the results into targeted improvements: a new training objective, a fine-tuned model, or a sharper benchmark - Diagnose systematic failure modes in production output and fix them at the root rather than patching symptoms - Build the tools and workflows that let the team iterate and validate fast - Partner with product and engineering to ship quality improvements that hold up at scale WHAT YOU'LL BRING - Hands-on experience with vision-language models or multimodal modeling: training, fine-tuning, or systematically evaluating them - Track record of building evaluations for subjective or hard-to-measure qualities, not just accuracy on labeled benchmarks - Experience with post-training techniques including supervised fine-tuning and reinforcement learning - Experience building AI systems, with production experience shipping models that real users depend on - Master’s or PhD in Computer Science, Machine Learning, or a related field, or equivalent hands-on research experience. A strong publication record at top-tier conferences such as NeurIPS, CVPR, ACL, or comparable venues. COMPENSATION RANGE: The base salary for this full-time position, which spans multiple internal levels depending on qualifications, ranges between $180K - $340K plus benefits & equity. Final offer amounts are determined by multiple factors, including but not limited to experience and expertise in the requirements listed above. If you're interested in this role but you don't meet every requirement, we encourage you to apply anyway! We're always excited about meeting great people.
Sales Enablement Manager
ABOUT US: Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. THE ROLE We're looking for a hands-on Sales Enablement Manager to help build GTM Enablement as Fireworks scales its go-to-market motion. The role sits in the Revenue Operations organization and reports to the Head of GTM Enablement. This is a builder's seat with a real delivery mandate. Upon arrival, you'll take immediate ownership of two flagship initiatives that define how Fireworks captures and serves its GTM knowledge: organizing our knowledge as a data product and industrializing our internal GTM Tutor App. But you won't live only in content and tooling. You're also expected to deliver traditional enablement, facilitating live and virtual sessions, supporting role-based ramp, and running cross-functional GTM programs that keep the field ahead of a fast-moving product. To do it well, you'll pair the fundamentals of great enablement (facilitation, content development, and program management) with genuine AI fluency, building the tools you teach rather than just briefing them. You'll own processes end to end and drive outcomes across Product, Marketing, and Sales without waiting to be told how. WHAT YOU'LL OWN 1. Own our knowledge as a data product: Take immediate ownership of our "knowledge as a data product" vision by structuring, governing, and maintaining GTM knowledge so it's accessible for both human and agent consumption. Complete a full audit of our existing knowledge and content, then define and roll out the taxonomy, standards, and governance for how we create, maintain, and retire content going forward, turning a scattered corpus into a trusted, queryable source of truth that stays healthy and current as it grows. 2. Industrialize the GTM Tutor App: Harden and scale our "GTM Tutor App," the internal AI application that trains and coaches sellers on GTM topics. Build the framework to add new topics quickly, improve the user experience, and strengthen the backend so the app is reliable, extensible, and ready for broad field adoption. 3. Build the cross-functional content engine: Establish the recurring processes and working relationships with Product, Marketing, Field Engineering, and Sales that ensure the field and our customers always have the sales and marketing assets they need. Create the intake, prioritization, and hand-off model that turns new product primitives, launches, and campaigns into field-ready and customer-facing collateral on a predictable cadence, and hold cross-functional partners accountable to it. 4. Deliver core enablement programs: Design and deliver enablement content and facilitate high-impact live and virtual sessions across product knowledge, sales skills, competitive positioning, and internal process. Support role-based ramp for new Account Executives, AI Field Engineers, and BDRs, and help run critical GTM programs alongside partners in Sales, Field Engineering, Marketing, and Product. As we grow, you'll expand our capacity to meet the field's enablement needs. 5. Codify field best practices: Synthesize what's working from real customer engagements and enablement outcomes, codify it into repeatable plays and content, and deliver it back to the field, feeding insight upstream to help shape the evolving GTM motion. 6. Help define the future of agentic enablement: Shape what agentic enablement looks like at Fireworks, designing and shipping AI agents that lift Account Executive efficiency and surface Revenue Operations insight, building on the foundations laid by our GTM Engineering team. MINIMUM QUALIFICATIONS - Several years (5+) of experience in GTM enablement, sales, or a related solutions/RevOps role, with demonstrated ownership of enablement programs end to end - Strong at both content development and live facilitation for audiences from new hires to experienced sellers - Has personally built AI agents or apps, through prompting, no-code, low-code, or code, to drive seller or operational productivity (engineering degree not required) - Excellent program and process management: you can stand up structure, standards, and repeatable workflows from scratch - Skilled at eliciting knowledge from busy subject-matter experts and turning it into structured, reusable content - Comfortable working with knowledge, content, and data as structured systems (taxonomy, governance, quality standards) - Proven ability to enable technical sellers to sell credibly to technical buyers - A trusted cross-functional partner who can work independently across Product, Marketing, and Sales - Thrives in ambiguity, high ownership, and the pace of a high-growth startup - Willing and able to travel ~15–20% for onboarding weeks, offsites, and kickoffs Preferred Qualifications - Prior experience as a quota-carrying field IC (Account Executive, AI Field Engineer, or similar) - Background in AI/ML infrastructure, developer tools, or API-first and consumption-based businesses, with literacy in usage-based pricing models - Experience with modern enablement tooling and systems (LMS, CMS, CRM, and enablement platforms) - Experience designing knowledge bases or content for retrieval by AI/LLM systems (RAG, agents) WHY FIREWORKS? - Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. - Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. - Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. - Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
Member of Technical Staff, Evals Platform
ABOUT US: Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. We are seeking a Member of Technical Staff, Evals & Post-Training Product to help define how developers improve models on Fireworks. This role sits at a unique intersection of scalable system design, deep data science, and model quality. You will build the infrastructure and workflows that connect evaluation and post-training. Our evaluation setup has grown past its original scope, and we need someone who can take it to the next stage, improving programmatic access and scale. You will work across backend systems, sandbox infrastructure, and user-facing surfaces to make it easier to author evals, understand results, and iterate quickly. KEY RESPONSIBILITIES - Scale Eval Infrastructure: Take ownership of our internal eval setup and evolve it for the future. You will design systems to eliminate single-host coordination bottlenecks, resolve log-syncing latency, and build seamless programmatic access. - Benchmark Obsession & Reproduction: Act as a "metrics obsessive." Track state-of-the-art (SOTA) benchmarks, read the latest research papers, dig deep into data discrepancies, and insist on rigorously reproducing published results internally. - Pioneer New Benchmarks: Design and build entirely new benchmarks to measure model performance on complex, emerging, or domain-specific use cases. - Own Fine-Tuning Product Experiences: Build and improve user-facing workflows for post-training, including fine-tuning experiences across SFT, RFT, and related model-improvement capabilities. - Work Closely With Users: Partner with customers and internal stakeholders to understand evaluation and fine-tuning needs, triage issues, and convert bespoke workflows into productized, reusable solutions. MINIMUM REQUIREMENTS - Experience: 1–7 years of software engineering or data science experience (We are hiring at multiple levels for this role). - Strong System Design Skills: You know how to architect scalable, programmatic systems and transition legacy setups into robust infrastructure. - Sandbox Infrastructure: Hands-on experience building or working with sandbox environments and sandbox infrastructure for secure code execution and testing. - Analytical & Data Science Mindset: You possess a deep understanding of LLM evaluations, how to design them, and how to use the results to guide model improvement. You are meticulous about data and metrics. - Understanding of the GenAI Lifecycle: You understand the end-to-end workflow—from prompting a base model to curating a dataset, fine-tuning, and productionizing agents. PREFERRED QUALIFICATIONS - Experience: 3+ years of software engineering or applied data science experience. - Frameworks & Orchestration: Experience working with the Harbor framework or similar container registry and orchestration tools. - Public Writing & Analysis: A strong interest in discovering where different models excel and fall short, with a desire to write up and publish these insights publicly (e.g., technical blogs, whitepapers). - Inference & Hardware Knowledge: Interest in the hardware side of AI—understanding GPU constraints, inference optimization techniques, and how they relate to model performance. - Startup DNA: Experience in fast-paced environments where you own features end-to-end. WHY FIREWORKS? - Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. - Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. - Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. - Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
Member of Technical Staff, AI Training Infrastruct...
ABOUT US: Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. THE ROLE: As a Training Infrastructure Engineer, you'll design, build, and optimize the infrastructure that powers our large-scale model training operations. Your work will be essential to developing high-performance AI training infrastructure. You'll collaborate with AI researchers and engineers to create robust training pipelines, optimize distributed training workloads, and ensure reliable model development. KEY RESPONSIBILITIES: - Design and implement scalable infrastructure for large-scale model training workloads - Develop and maintain distributed training pipelines for LLMs and multimodal models - Optimize training performance across multiple GPUs, nodes, and data centers - Implement monitoring, logging, and debugging tools for training operations - Architect and maintain data storage solutions for large-scale training datasets - Automate infrastructure provisioning, scaling, and orchestration for model training - Collaborate with researchers to implement and optimize training methodologies - Analyze and improve efficiency, scalability, and cost-effectiveness of training systems - Troubleshoot complex performance issues in distributed training environments MINIMUM QUALIFICATIONS: - Bachelor's degree in Computer Science, Computer Engineering, or related field, or equivalent practical experience - 3+ years of experience with distributed systems and ML infrastructure - Experience with PyTorch - Proficiency in cloud platforms (AWS, GCP, Azure) - Experience with containerization, orchestration (Kubernetes, Docker) - Knowledge of distributed training techniques (data parallelism, model parallelism, FSDP) PREFERRED QUALIFICATIONS: - Master's or PhD in Computer Science or related field - Experience training large language models or multimodal AI systems - Experience with ML workflow orchestration tools - Background in optimizing high-performance distributed computing systems - Familiarity with ML DevOps practices - Contributions to open-source ML infrastructure or related projects WHY FIREWORKS? - Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. - Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. - Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. - Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
Manager, Field Engineering
ABOUT US: Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. We're hiring a Manager, Field Engineering to lead a team of Field Engineers driving technical evaluations and production adoption of Fireworks' inference and fine-tuning platform. This is a player-coach role — you'll manage and grow a distributed team of hands-on engineers while staying deeply technical yourself, leading engagements end-to-end at some of the most ambitious AI-native companies and enterprises. As Manager you will be a critical link between the field and the rest of the company: coaching your field engineers in real time, unblocking complex evaluations, and ensuring every customer engagement meets a high technical bar across latency, throughput, cost, security, and scalability. You'll work closely with regional sales leadership to scale the engagement model, codify the playbook, and build the team. This is a hands-on leadership role. Your credibility with field engineers comes from having built production AI systems with customers, not just having managed people who did. You will still get in the weeds when the stakes are highest with shipping POCs, performance optimizations, co—lead training engagements with our research team and debugging alongside your team. WHAT YOU'LL DO - Lead, coach, and grow a distributed team of Field Engineers — hiring, onboarding, developing, and holding a high bar for technical excellence and customer outcomes - Own your team's engagement portfolio: allocate the right engineers to the right pursuits and ensure consistent execution across discovery, demos, POCs, and production integrations - Lead complex evaluations end-to-end (discovery → architecture → POC → production plan), personally stepping in on the highest-stakes or most technically challenging deals - Coach AEs and Field Engineers in real time to improve deal quality and close outcomes — reviewing architectures, sitting in on calls, and running sharp post-mortems on wins and losses - Build and refine the Field Engineering playbook: discovery frameworks, POC templates, reference architectures, and reusable field artifacts - Serve as the voice of your team and customers internally — systematizing field insights, influencing the product/engineering roadmap, and translating recurring pain points into concrete platform improvements - Partner with revenue leadership on pipeline health, forecast calls, and territory planning for your region - Stay hands-on when it matters: build/ship alongside your engineers (POCs/MVPs, load testing, eval + fine-tuning pipelines, model-serving choices across vLLM/SGLang/TensorRT-LLM) and ensure strong post-sales handoffs for onboarding and adoption - Track and improve your team's operating metrics — win rates and velocity, POC cycle time, utilization, and customer adoption outcomes YOU MAY BE A FIT IF - You have 8+ years of overall experience, including 2+ years managing Field Engineering, Solutions Engineering, Forward Deployed Engineering, or Pre-Sales teams, with hands-on experience in enterprise software or AI infrastructure - You have a strong technical foundation and fluency in the LLM stack: inference trade-offs, model serving, fine-tuning workflows (SFT; DPO/RFT a plus), and deploying models on GPU infrastructure across AWS, Azure, and GCP - You have demonstrated ability to build production software with customers, not just advise on it — you have shipped code running in someone else's production environment and can still do it when a deal demands it - You have a track record of developing engineers — coaching, giving direct feedback, and growing individual contributors into senior and lead roles - You have proven your ability to partner effectively with Sales while maintaining technical integrity and customer trust in high-stakes deal environments - You have strong communication skills — able to run a sharp discovery call, present to a VP or CTO, and debug a latency issue with an ML engineer in the same afternoon - You have a builder mindset: you thrive in fast-moving, startup environments and enjoy creating structure where little exists - Willingness to travel (up to ~30%) for customer engagements and team onsites YOU EXCEL IN THESE KEY COMPETENCIES - Technical depth with coaching instinct: able to both architect the solution and teach the engineer next to you why it's right - Operational rigor: able to run a portfolio of concurrent engagements and hit consistent execution across the team - Deep familiarity with the AI inference, fine-tuning, and production GenAI market and ecosystem - Sophisticated understanding of enterprise cloud platforms and complex integration environments, with the ability to translate technical architecture into business value - Strong written and verbal communication — internal docs, exec-ready summaries, and customer-facing technical artifacts that are sharp and clear - Commercial instincts: able to size opportunities, understand deal dynamics, and align your team's effort to revenue acceleration - Comfort with ambiguity: creates process where none exists without slowing the team down OUR MISSION & CULTURE Our mission is to make AI inference fast, affordable, and production-ready for every developer and enterprise. Our organization is flat, and our team is small and talent dense. We particularly like people who are truth-seeking, passionate, and creative. We enjoy spirited debate, crazy ideas, and shipping code. We are building a Field Engineering organization in that same image — engineers who lead with credibility earned in the codebase and the customer's infrastructure, who compress the feedback loop from field to roadmap, and who treat every customer deployment as a chance to make the platform better for everyone. WHY FIREWORKS? - Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. - Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. - Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. - Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
Developer Advocate
ABOUT US: Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. ABOUT THIS ROLE Developers decide what infrastructure wins. They try three platforms in an afternoon, keep the one that got them to a working inference call fastest, and tell everyone else about it. As a Developer Advocate at Fireworks, you own a piece of that decision: you make Fireworks the platform developers reach for first when they want to build, fine-tune, and serve frontier open models in production. You do that by building things worth showing, not by talking about things other people built. This role has a dual bar, and we mean both halves of it. You are a working engineer: you ship code, read diffs, deploy models, and can hold your own in a conversation about batching, quantization, or why a fine-tune regressed. You are also a public technical communicator: you write clearly enough that a senior engineer forwards it, you're good on a stage, and you have or want a real presence in the places developers actually argue about this stuff. Most people are strong at one and passable at the other. We're looking for the rarer profile who is genuinely credible at both, because in AI infrastructure the audience can tell within one paragraph whether you've actually run the thing. The field moves weekly. A major open-weights model drops and the window to be the definitive resource on it is measured in days. Your job is to be one step ahead of that cadence, consistently, and to make it look repeatable rather than heroic. This is one of the first advocate seats on the team, which means you'll help set the standards: for content, for launch response, and for how we show up. We're hiring at either the Senior or Staff level and will determine level through the interview process based on your experience. LOCATION AND WORK STYLE This role is based in San Mateo, CA or London. You'll travel regularly to the conferences, hackathons, meetups, and partner events where the developer community shows up. Expect roughly 20% to 25% travel in a typical quarter, concentrated around major events and launches. If you're based in London, you'll anchor our presence in the European developer community, work across time zones with a primarily US-based team, and travel periodically to our San Mateo HQ for onsites and planning. WHAT YOU'LL DO Build things developers can run. Ship cookbooks, quickstarts, reference applications, and end-to-end tutorials that take a developer from zero to a working inference call and then to something production-shaped. Everything you publish is code someone can fork and run, not a screenshot of code that worked once on your machine. The bar is content a senior engineer respects and a new developer can follow, ideally both in the same artifact. Own the launch response. When a significant model or technique lands, you're among the first credible voices with a working demo, an honest benchmark, and a "how to actually use this today" guide. You'll help build the internal loop (model access, eval harnesses, publishing paths) that makes day-one content a process rather than an all-nighter. Show up where developers are, owned and unowned. On our properties, that's docs, blog, examples, cookbooks, Discord, and the developer newsletter. On properties we don't own, that's GitHub, X, Reddit, YouTube, Hacker News, and the open-source projects our developers depend on, including contributing code and reviewing PRs where it matters. You answer real questions in public and turn the recurring ones into permanent content. Speak, teach, and run the room. Conference talks, hands-on workshops, hackathon mentoring, and developer dinners. You'll help decide where Fireworks shows up and what we say when we get there, and you'll build the material so the next person can deliver it without starting over. Be the developer's voice inside Fireworks. Bring friction, feature gaps, and unmet needs back to product and engineering with evidence: a failing repro, a thread of five people hitting the same wall, a competitor's ergonomics that are simply better. Then close the loop publicly when we ship the fix. Measure what you do. You'll instrument your own work: what content drove signups, which events produced developers who actually built something, where people fall out between first call and production. We care about adoption, activation, and time to first successful build. We do not care about impressions, booth scans, or attendance counts, and we won't ask you to report them. WHAT SUCCESS LOOKS LIKE IN THE FIRST YEAR - You've published a body of technical work (tutorials, apps, deep dives) that developers cite and competitors read. - When a major model drops, Fireworks has authoritative, runnable content out fast enough to be part of the conversation rather than a follow-up to it. - You have a credible, recognized presence in developer communities where we weren't previously part of the discussion. - You've spoken at multiple significant events and left behind a workshop or talk that others on the team can now deliver. - Product and engineering can point to specific roadmap decisions that changed because of feedback you surfaced. - The content and launch standards you set are what new advocates onboard into, and they hold up without you in the room. WHAT WE'RE LOOKING FOR - 5+ years of combined engineering and developer-facing experience, with real time in both. For example, several years shipping production software plus meaningful time in DevRel, developer education, technical writing, or solutions engineering. We're flexible on the ratio and inflexible on the requirement that both are genuinely there. - You can code and read diffs. Strong Python; comfortable in TypeScript/JavaScript. You've built and deployed applications yourself, and you can review someone else's sample and tell whether it's good. - Hands-on with the modern AI stack. You've worked with LLM APIs in production, built with at least one agent framework, and have personally fine-tuned, evaluated, and deployed a model, not just read about it. You understand what inference latency, throughput, and cost actually depend on. - A public track record. Technical writing, talks, videos, open-source work, or a following you earned. Something we can go read or watch that shows you can explain hard things well to engineers. - Demonstrated speaking ability. You've presented at conferences, meetups, or workshops, and you're comfortable doing it on short notice about something that shipped last week. - Learning velocity. You can go from "this paper came out Tuesday" to "here's a working implementation and an honest assessment" quickly, and you have a system for staying current rather than relying on luck. - Ownership instinct. You pick your own topics, scope your own projects end to end, and don't need a content calendar handed to you. This role is early enough that you'll be defining the work as often as executing it. BONUS POINTS - Open-source contributions or maintainership in the AI/ML ecosystem, including inference engines, model libraries, agent frameworks, and evaluation tooling. - Depth in inference optimization, serving architecture, post-training, or evaluation methodology. - You've built something that outlived your direct involvement: a content operation, a launch process, an events playbook, or a community program. - Comfort making video. - Existing relationships with AI research or open-model communities. - You use frontier coding agents in your daily work and have opinions about where they help and where they don't. - Experience being early on a DevRel team and helping establish how it operates. WHY FIREWORKS? - Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. - Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. - Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. - Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.
Helix AI Engineer, Training Performance
Figure is an AI robotics company developing autonomous general-purpose humanoid robots. The goal of the company is to ship humanoid robots with human level intelligence. Its robots are engineered to perform a variety of tasks in the home and commercial markets. Figure is headquartered in San Jose, CA. Figure's vision is to deploy autonomous humanoids at a global scale. Our Helix team is looking for an experienced AI Training Performance Engineer to take our model training to the next level. This role is focused on improving distributed training frameworks for large scale model training, optimizing GPU kernels, exploring the relative gains of different accelerator types and co-designing our models to maximize utilization of our hardware. Responsibilities - Optimize training performance for a 100B+ parameter models across 100k+ GPUs. - Collaborate with the broader team on accelerator choice, cluster topology, scheduling, and hardware procurement decisions to inform future scaling. - Write and optimize custom kernels (Triton/CUDA) - Build tooling and dashboards for continuous performance monitoring, regression detection, and root-cause analysis across training jobs - Optimize data loading and preprocessing pipelines so I/O never gates the accelerators - Improve checkpointing, fault tolerance, and elastic restart so large jobs recover quickly from node failures without losing significant wall-clock time - Partner with researchers to co-design model architectures and training recipes that are performant at scale (e.g., activation checkpointing strategies, mixed precision, sequence packing) - Extend and contribute to kernel compilers (e.g., Triton, Gluon) to improve iteration speed and enable targeting of custom/non-NVIDIA accelerators - Build and extend agentic systems that automatically generate, benchmark, and iterate on custom kernels - Evaluate emerging accelerator architectures (AMD, TPU, SRAM-based ASICs, and other novel hardware) for fit with our training workloads, and lead proof-of-concept ports/benchmarks - Explore different model/data parallelisms (FSDP, context parallel, expert parallel, etc.) to determine optimal configuration per model size. Requirements - Bachelor's or Master's degree in Computer Science, Computer/Electrical Engineering, or a related field - 3+ years in AI performance engineering, with significant time leading large-scale performance improvement projects - Deep understanding of GPU architecture and performance characteristics (memory bandwidth, compute-bound vs. memory-bound ops, occupancy) - Proficiency with profiling tools (Nsight Systems/Compute, PyTorch Profiler, HTA, or similar) and ability to translate traces into concrete optimizations - Solid grasp of collective communication (NCCL) and modern networking concepts (RDMA, NVLink, InfiniBand/RoCE, topology-aware placement). - Strong Python and CUDA/C++ skills; comfortable reading and modifying framework internals - Experience debugging performance regressions and instability at scale (stragglers, hangs, OOMs, numerical divergence) - Experience defining and reasoning about hardware-efficiency metrics (MFU/HFU) and using them to drive optimization priorities Bonus Qualifications - Experience with heterogeneous or multi-datacenter training setups and cross-cluster orchestration - Contributions to open-source ML systems projects (PyTorch, Megatron-LM, vLLM, DeepSpeed, JAX, etc.) - Exposure to non-NVIDIA accelerators (AMD GPUs, TPU/Trainium/Inferentia, or custom silicon) and heterogeneous fleet management. The US base salary range for this full-time position is between $200,000 - $400,000 annually. The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.
Staff ML Systems Engineer, Distributed Systems
FieldAI’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field. We are seeking a Staff ML Systems Engineer to architect and build the distributed infrastructure that powers large-scale machine learning workflows across the organization. This role sits at the intersection of machine learning, distributed systems, and platform engineering. You will be responsible for designing scalable systems that support data processing, model training, evaluation, and post-processing pipelines while enabling ML teams to efficiently develop, operate, and scale production-grade workflows. You will play a critical role in defining the architectural patterns, tooling, and infrastructure that underpin our machine learning platform. What You'll Get To Do Design and build scalable distributed machine learning pipelines across data processing, model training, evaluation, and post-processing workflows. Architect distributed execution systems, including parallelization strategies, workload scheduling, resource allocation, and fault tolerance mechanisms. Develop reusable abstractions, frameworks, and libraries that simplify distributed pipeline development. Optimize performance across distributed CPU and GPU environments, improving throughput, utilization, and reliability. Design systems that effectively manage data partitioning, memory utilization, serialization overhead, and compute efficiency. Partner closely with ML engineers, data engineers, and infrastructure teams to productionize research workflows and enable large-scale model development. Establish best practices and engineering standards for distributed machine learning infrastructure. Evaluate and guide decisions around distributed computing frameworks, infrastructure technologies, and system design trade-offs. Improve observability, debugging, monitoring, and operational tooling for distributed systems at scale. What You Have 5+ years of experience building distributed systems, backend infrastructure, machine learning platforms, or large-scale data processing systems. Strong Python programming skills, including experience with concurrency, performance optimization, and systems development. Experience with distributed computing frameworks such as Ray, Spark, Dask, Flink, or similar technologies. Experience designing and scaling data pipelines or machine learning workflows. Strong system design skills with demonstrated expertise in scalability, reliability, and performance optimization. Experience diagnosing and resolving bottlenecks in distributed environments. Ability to work cross-functionally and drive technical decisions across multiple teams. The Extras That Set You Apart Experience building infrastructure for machine learning training and inference systems. Familiarity with modern ML frameworks such as PyTorch or TensorFlow. Experience with multi-node or multi-GPU training architectures, including DDP, FSDP, DeepSpeed, or similar technologies. Experience operating Kubernetes-based infrastructure and large-scale cloud systems. Deep understanding of distributed systems concepts including data locality, serialization costs, scheduling, and resource management. Experience with distributed debugging, observability, and workflow orchestration platforms. Proven ability to establish technical direction and influence architecture across organizations.
Senior Research Engineer, Manipulation
FieldAI is transforming how robots interact with the real world. Our growing R&D team is based in Boston, where we develop risk-aware, reliable, field-ready AI systems that tackle the hardest problems in robotics and unlock the potential of embodied intelligence. We take a pragmatic approach that goes beyond off-the-shelf, purely data-driven methods or transformer-only architectures, combining cutting-edge research with real-world deployment. Our solutions are already deployed globally, and we continuously improve model performance through rapid iteration driven by real field use. We are seeking a Research Engineer to join our manipulation efforts. In this role, you will work at the intersection of robotics research and applied engineering, building, training, testing, and refining large-scale learned manipulation models and capabilities that accelerate autonomous control and loco-manipulation on humanoid robots. You will collaborate closely with research scientists, engineers, and product partners to design novel manipulation strategies and deliver systems that directly feed into FieldAI’s robot learning pipelines. You will also play a key role in advancing robotics foundation models designed to be generalizable across embodiments, with an initial focus on humanoid platforms. The work will emphasize combinations of learned and physically-grounded models, alongside the data and training systems required to scale them. This role is about pushing the frontier of manipulation research while ensuring that breakthroughs translate into practical, scalable autonomy in real-world environments. What You’ll Get To Do Advance Humanoid Manipulation Research and Development Design, implement, and evaluate learning-based manipulation models and strategies for humanoid robots across a wide range of tasks. Drive projects from early concepts through on-robot testing and deployment. Develop loco-manipulation capabilities that integrate perception, control, and planning. Drive Robotics Foundation Model Development Contribute to foundation models for manipulation, working on model architecture, data collection, large-scale training pipelines, and deployment infrastructure. Ensure model development supports generalization and transfer across diverse robotic platforms. Collaborate with research scientists to integrate large-scale manipulation data into learning pipelines powering foundation models. Train and evaluate manipulation models using imitation learning, reinforcement learning, and other training approaches. Build Systems That Bridge Research and Deployment Translate research ideas into reliable robotic systems that operate in real-world conditions. Ensure systems are robust, reproducible, and aligned with data collection and learning objectives. Develop experimental infrastructure to support rapid iteration, large-scale training, and evaluation. Collaborate Across Disciplines Partner with mechanical and electrical engineers on hardware integration and system bring-up. Work closely with field teams to refine interfaces and improve manipulation performance. Act as a connective layer between autonomy research and applied robotics engineering. Rapidly Iterate and Deliver Prototype quickly, run experiments on hardware, and validate results in the field. Balance exploratory research with concrete deliverables that support near-term goals. Debug complex system-level issues spanning software, hardware, data, training infrastructure, and learning. What You Have Bachelor’s, Master’s, or PhD in Robotics, Computer Science, Mechanical Engineering, or a related field. 2+ years of hands-on experience in robotic manipulation and/or robot learning in academic or industry settings. Strong foundation in robot kinematics, dynamics, and control, with a focus on manipulation. Strong background in learning-based manipulation, with an emphasis on reinforcement learning and imitation learning, and experience training modern deep-learning models. Experience using frameworks such as PyTorch and building reproducible model-training and evaluation pipelines. Proven experience implementing and evaluating robotic systems on real hardware. Ability to work effectively in fast-paced, highly collaborative environments. Curiosity, ownership, and the confidence to challenge assumptions and propose new approaches. The Extras That Set You Apart Experience working with humanoid robots or multi-fingered robotic hands. Experience training large-scale robot foundation models. Experience with reinforcement-learning fine-tuning, offline RL, and related policy training methods. Familiarity with large-scale data collection, spanning simulation, web-scale data, human-in-the-loop data, and autonomously-collected data. Publications or open-source contributions in top robotics or machine-learning venues. Experience with large-scale robotics data collection and dataset management. Strong interest in bridging cutting-edge research with field-ready robotic systems.
Robotics Autonomy Engineer, Locomotion
FieldAI’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field. About the Job Field AI is building the future of autonomy—from rugged terrain to real-world deployment. We’re on a mission to develop intelligent, adaptable robotic systems that operate beyond simulation and thrive in unpredictable environments. As our Robotics Autonomy Engineer – Locomotion, you’ll lead the development and deployment of state-of-the-art controllers for legged and humanoid robots. You’ll be part of a deeply technical team advancing real-world robotic capabilities through cutting-edge research, simulation tools, and field validation. If designing locomotion systems that can navigate complex, dynamic environments excites you, and you want to work where your code hits the ground (literally)—this is your role. This is Field AI. What You’ll Get To Do Architect and implement scalable reinforcement learning (RL) pipelines for locomotion and whole-body control on legged and humanoid robots Design policy architectures, training curricula, and domain randomization strategies that close the sim-to-real gap Integrate GPU-accelerated, physics-based simulation environments with custom, distributed training workflows Train locomotion policies from human motion data using imitation learning and motion retargeting, and distill them into compact policies that run on the robot Create agile, robust, and terrain-aware (perceptive) locomotion behaviors for quadruped and humanoid platforms, and validate them on real hardware Solve real-world challenges in balance and push recovery, contact-rich dynamics, high degree of freedom (DOF) whole-body coordination, and terrain variability Automate evaluation across domain-randomized scenarios and maintain simulation infrastructure that enables rapid prototyping, validation, and reproducibility Work closely with systems engineers, perception experts, and embedded teams, incorporating real-world telemetry and field data to continuously improve generalization Lead deployment workflows from experiment through lab testing to field robot validation What You Have Master’s degree or higher in Robotics, Computer Science, Engineering, or related field (PhD strongly preferred) Deep expertise in reinforcement learning for continuous control 2+ years of experience developing and deploying locomotion policies on real robotic systems (preferred) Hands-on experience with legged robot platforms (quadrupeds, bipedal/humanoid systems, or exoskeletons) Proficiency with simulation tools such as Isaac Gym, Isaac Lab, MuJoCo, or PyBullet Strong command of sim-to-real transfer and a track record of bridging the gap successfully Solid understanding of contact dynamics, control theory, and kinematics Strong Python and/or C++ development skills in Linux-based development environments Familiarity with machine learning frameworks (PyTorch, JAX, TensorFlow) A passion for building things that move in the real world The Extras That Set You Apart 3+ years of experience in an industry or startup robotics setting Experience optimizing and deploying learning based controllers on resource constrained robotic platforms (ONNX Runtime, NVIDIA TensorRT, real time onboard inference) Publications or open-source contributions in locomotion, reinforcement learning, or control (e.g., CoRL, RSS, ICRA, IROS) Familiarity with ROS/ROS2 or custom middleware for real-time control Background in manipulation, loco-manipulation, or whole-body coordination Experience combining learned policies with model predictive control (MPC) or whole-body controllers Experience with motion imitation pipelines from motion capture or teleoperation data Experience debugging sim-to-real issues at scale Contributions to reinforcement learning libraries or simulation platforms Prior work on multi-agent learning or terrain-adaptive control systems
Applied Researcher - Deployment Intelligence
Dyna Robotics builds general-purpose robots powered by a proprietary embodied AI foundation model with top-in-industry generalization and real-world performance. Already deployed with customers across multiple industries, our robots do commercial-grade work in the physical world. Our team comes from Google DeepMind, Meta, and Cruise, and we're backed by CRV, First Round, and other leading investors. THE ROLE Our models don't stop learning at deployment. A growing fleet of robots is generating real production data every day, and the gap between "works in the lab" and "works at a new customer site, forever" is a research problem, not just an ops one. As an Applied Researcher on the AI Research team, you'll own that gap: mining fleet sensor and video data for failure modes, building the monitoring that catches problems before customers do, and turning deployment data into continuous, measurable model improvement. This is a hands-on, ship-it role. We care far more about whether you can land a real improvement on the fleet than about producing research for its own sake. WHAT YOU'LL DO - Continuous Learning Loops: Design and ship pipelines that turn real deployment data (successes, failures, teleop corrections) into targeted fine-tuning and online policy improvement, closing the loop from field to model without a full retrain cycle every time. - Fleet Data Analytics: Mine high-frequency multimodal sensor and video data across tens of thousands of fleet episodes to catch failure modes, drift, and regressions before they become customer-visible. - RL for Deployment: Apply reinforcement learning (offline RL, RL fine-tuning, reward modeling from human and teleop feedback) to improve policies directly from real-world deployment data, not just simulation. - Automated Fleet Monitoring: Build automated monitoring that flags anomalies, near-failures, and out-of-distribution scenes across the fleet in real time, and that decides what needs a human versus what the system can self-correct. - Cross-Scene Generalization: Characterize and close generalization gaps as robots move to new sites, lighting, layouts, and objects; build the evaluation harnesses and data-selection strategies that make day-one performance at a new customer site predictable. - End-to-End Ownership: Partner with Research, Data, and Deployment teams to turn a finding into a shipped improvement, from a data-analysis notebook to a production monitoring dashboard to a deployed model update. WHAT YOU'LL BRING - Bias to Ship: You're happiest closing the loop, landing a fix on the fleet and watching it hold up at a real customer site, rather than polishing a benchmark number or a paper. We want someone driven by shipped impact and genuine passion for the problem, not research for its own sake. - Educational Background: Bachelor's, Master's, or PhD in CS, Robotics, Statistics, or a related field, or equivalent practical experience. Degree level doesn't matter to us; what matters is genuine passion for the work and a track record of hands-on effort that shipped into a real system, not just a benchmark. - Applied ML Depth: Hands-on experience in at least two of: reinforcement learning, sensor-data modeling/anomaly detection, vision-language models, or continual/online learning. - Production Instincts: Experience building monitoring, evaluation, or data pipelines for a live ML system, comfortable with the ambiguity of real-world fleet data versus curated benchmarks. - Experimentation & Statistics: Comfortable designing and reading production experiments (A/B tests, canary rollouts, staged fleet deployments) and applying enough statistical rigor to tell a real regression from noise in messy real-world data. - Technical Stack: Strong Python and PyTorch (or JAX); comfortable with large multimodal datasets and distributed compute (Slurm/GPU clusters). - Communication: Able to turn a fleet-scale data investigation into a clear recommendation that researchers and operators can act on. BONUS POINTS FOR - Experience with robot fleets or other physically-deployed autonomous systems in the field, not just simulation. - Experience building or fine-tuning perception or foundation models for automated monitoring, captioning, or anomaly detection. - Background in statistical methods for detecting anomalies and drift (change-point detection, forecasting) applied to sensor or telemetry data. - Experience with human-in-the-loop learning: reward modeling from operator corrections, active learning, or data curation from failure cases. At Dyna Robotics, we build technology for the real world, which requires a team as diverse as the environments our robots inhabit. We are an equal opportunity employer committed to technical rigor and mutual respect. Don’t let a checklist stop you. Data shows that underrepresented groups often only apply if they meet 100% of the criteria. We value problem-solving and grit over keyword matching. If you’re passionate about robotics, no matter your discipline, we want to hear from you, even if you don't check every box.
Research Staff, LLMs
COMPANY OVERVIEW Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text (STT), text-to-speech (TTS), and building production-grade voice agents at scale. More than 200,000 developers and 1,300+ organizations build voice offerings that are ‘Powered by Deepgram’, including Twilio, Cloudflare, Sierra, Decagon, Vapi, Daily, Cresta, Granola, and Jack in the Box. Deepgram’s voice-native foundation models are accessed through cloud APIs or as self-hosted and on-premises software, with unmatched accuracy, low latency, and cost efficiency. Backed by a recent Series C led by leading global investors and strategic partners, Deepgram has processed over 50,000 years of audio and transcribed more than 1 trillion words. There is no organization in the world that understands voice better than Deepgram. COMPANY OPERATING RHYTHM At Deepgram, we expect an AI-first mindset—AI use and comfort aren’t optional, they’re core to how we operate, innovate, and measure performance. Every team member who works at Deepgram is expected to actively use and experiment with advanced AI tools, and even build your own into your everyday work. We measure how effectively AI is applied to deliver results, and consistent, creative use of the latest AI capabilities is key to success here. Candidates should be comfortable adopting new models and modes quickly, integrating AI into their workflows, and continuously pushing the boundaries of what these technologies can do. Additionally, we move at the pace of AI. Change is rapid, and you can expect your day-to-day work to evolve just as quickly. This may not be the right role if you’re not excited to experiment, adapt, think on your feet, and learn constantly, or if you’re seeking something highly prescriptive with a traditional 9-to-5. THE OPPORTUNITY Voice is the most natural modality for human interaction with machines. However, current sequence modeling paradigms based on jointly scaling model and data cannot deliver voice AI capable of universal human interaction. The challenges are rooted in fundamental data problems posed by audio: real-world audio data is scarce and enormously diverse, spanning a vast space of voices, speaking styles, and acoustic conditions. Even if billions of hours of audio were accessible, its inherent high dimensionality creates computational and storage costs that make training and deployment prohibitively expensive at world scale. We believe that entirely new paradigms for audio AI are needed to overcome these challenges and make voice interaction accessible to everyone. THE ROLE Deepgram is currently looking for an experienced researcher to who has worked extensively with Large Language Models (LLMS) and has a deep understanding of transformer architecture to join our Research Staff. As a Member of the Research Staff, this individual should have extensive experience working on the hard technical aspects of LLMs, such as data curation, distributed large-scale training, optimization of transformer architecture, and Reinforcement Learning (RL) training. THE CHALLENGE We are seeking researchers who: - See "unsolved" problems as opportunities to pioneer entirely new approaches - Can identify the one critical experiment that will validate or kill an idea in days, not months - Have the vision to scale successful proofs-of-concept 100x - Are obsessed with using AI to automate and amplify your own impact If you find yourself energized rather than daunted by these expectations—if you're already thinking about five ideas to try while reading this—you might be the researcher we need. This role demands obsession with the problems, creativity in approach, and relentless drive toward elegant, scalable solutions. The technical challenges are immense, but the potential impact is transformative. WHAT YOU'LL DO - Brainstorming and collaborating with other members of the Research Staff to define new LLM research initiatives - Broad surveying of literature, evaluating, classifying, and distilling current methods - Designing and carrying out experimental programs for LLMs - Driving transformer (LLM) training jobs successfully on distributed compute infrastructure and deploying new models into production - Documenting and presenting results and complex technical concepts clearly for a target audience - Staying up to date with the latest advances in deep learning and LLMs, with a particular eye towards their implications and applications within our products YOU'LL LOVE THIS ROLE IF YOU - Are passionate about AI and excited about working on state of the art LLM research - Have an interest in producing and applying new science to help us develop and deploy large language models - Enjoy building from the ground up and love to create new systems. - Have strong communication skills and are able to translate complex concepts clearly - Are highly analytical and enjoy delving into detailed analyses when necessary IT'S IMPORTANT TO US THAT YOU HAVE - 3+ years of experience in applied deep learning research, with a solid understanding toward the applications and implications of different neural network types, architectures, and loss mechanism - Proven experience working with large language models (LLMs) - including experience with data curation, distributed large-scale training, optimization of transformer architecture, and RL Learning - Strong experience coding in Python and working with Pytorch - Experience with various transformer architectures (auto-regressive, sequence-to-sequence.etc) - Experience with distributed computing and large-scale data processing - Prior experience in conducting experimental programs and using results to optimize models IT WOULD BE GREAT IF YOU HAD - Deep understanding of transformers, causal LMs, and their underlying architecture - Understanding of distributed training and distributed inference schemes for LLMs - Familiarity with RLHF labeling and training pipelines - Up-to-date knowledge of recent LLM techniques and developments THE CHALLENGE We are seeking researchers who: - See "unsolved" problems as opportunities to pioneer entirely new approaches - Can identify the one critical experiment that will validate or kill an idea in days, not months - Have the vision to scale successful proofs-of-concept 100x - Are obsessed with using AI to automate and amplify your own impact If you find yourself energized rather than daunted by these expectations—if you're already thinking about five ideas to try while reading this—you might be the researcher we need. This role demands obsession with the problems, creativity in approach, and relentless drive toward elegant, scalable solutions. The technical challenges are immense, but the potential impact is transformative. WHAT YOU'LL DO - Brainstorming and collaborating with other members of the Research Staff to define new LLM research initiatives - Broad surveying of literature, evaluating, classifying, and distilling current methods - Designing and carrying out experimental programs for LLMs - Driving transformer (LLM) training jobs successfully on distributed compute infrastructure and deploying new models into production - Documenting and presenting results and complex technical concepts clearly for a target audience - Staying up to date with the latest advances in deep learning and LLMs, with a particular eye towards their implications and applications within our products YOU'LL LOVE THIS ROLE IF YOU - Are passionate about AI and excited about working on state of the art LLM research - Have an interest in producing and applying new science to help us develop and deploy large language models - Enjoy building from the ground up and love to create new systems. - Have strong communication skills and are able to translate complex concepts clearly - Are highly analytical and enjoy delving into detailed analyses when necessary IT'S IMPORTANT TO US THAT YOU HAVE - 3+ years of experience in applied deep learning research, with a solid understanding toward the applications and implications of different neural network types, architectures, and loss mechanism - Proven experience working with large language models (LLMs) - including experience with data curation, distributed large-scale training, optimization of transformer architecture, and RL Learning - Strong experience coding in Python and working with Pytorch - Experience with various transformer architectures (auto-regressive, sequence-to-sequence.etc) - Experience with distributed computing and large-scale data processing - Prior experience in conducting experimental programs and using results to optimize models IT WOULD BE GREAT IF YOU HAD - Deep understanding of transformers, causal LMs, and their underlying architecture - Understanding of distributed training and distributed inference schemes for LLMs - Familiarity with RLHF labeling and training pipelines - Up-to-date knowledge of recent LLM techniques and developments - Published papers in Deep Learning Research, particularly related to LLMs and deep neural networks - Published papers in Deep Learning Research, particularly related to LLMs and deep neural networks Notice: We're aware of individuals impersonating Deepgram recruiters. All legitimate Deepgram recruiting communication comes from an @deepgram.com http://deepgram.com email address. If you've received a message claiming to be Deepgram, please forward it to careers@deepgram.com.
Senior Research Engineer, Safety
About Decagon Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences. Our technology enables industry-defining enterprises like Avis Budget Group, Block’s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel. We’re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We’re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others. We’re an in-office company, driven by a shared commitment to excellence and velocity. Our values — Just Get It Done, Invent What Customers Want, Winner’s Mindset, and The Polymath Principle — shape how we work and grow as a team. About the Team Read more about the research team's work here: https://decagon.ai/blog/introducing-decagon-labs The Research team develops the model and decision-making stack that powers Decagon’s conversational agents for enterprise support. We research, adapt, and implement state-of-the-art techniques in model training, prompting, orchestration, and evaluation in order to make our agents more accurate, robust, and efficient in real-world deployments. Our goal is to push the frontier of applied conversational AI: agents that reliably understand nuanced intent, track long context, and take the right actions under uncertainty. We measure success the way customers feel it: higher resolution rates, better user satisfaction, and consistent behavior at scale. About the Role As a Research Engineer focused on Safety, you’ll be responsible for making Decagon’s AI agents safe, reliable, and controllable, from evaluation through production. You’ll identify real-world failure modes and build the models, evaluations, and safeguards that prevent them. We’re looking for strong engineers who want to advance applied AI safety in production. People here own their work end-to-end, ship real improvements, and are trusted to make high-impact technical decisions. In this role, you will - Research and build safeguards against prompt injection, unsafe tool use, sensitive-data disclosure, policy violations, and hallucinated commitments - Build adversarial evaluations, simulations, red-team datasets, and regression suites informed by production failures - Develop and deploy classifiers, judges, reward signals, post-training methods, and runtime safeguards for safer agent behavior - Analyze production traces and incidents to identify root causes, test mitigations, and measure their impact - Partner with Security, Product, Infrastructure, Legal, and customer-facing teams to turn enterprise requirements into scalable safeguards and rollout practices Your background looks something like this - 4+ years of experience in AI/ML engineering, research, or AI safety - Hands-on experience evaluating, post-training, or deploying language models or agentic systems - Experience with modern post-training techniques, such as reinforcement learning, preference optimization, distillation, model routing, and synthetic-data generation - Experience with adversarial testing, model red teaming, prompt injection, policy enforcement, privacy, or safe tool use - Fluency in Python and modern ML tooling, with strong experimental judgment and the engineering depth to ship production systems - Comfort owning ambiguous, high-stakes technical problems and making clear risk and product tradeoffs Even better if you have - Experience building safeguards for high-stakes or regulated enterprise workflows - Familiarity with human-in-the-loop review, incident response, or responsible rollout frameworks for ML systems Compensation $200K – $400K + Offers Equity This range reflects the expected compensation for this role. Compensation within the range is determined based on experience, skills, and the scope of responsibilities, with flexibility for candidates who demonstrate exceptional impact. In addition to base salary, we offer competitive equity. Final compensation may vary based on location within the United States. Benefits We proudly offer the following benefits for our full-time employees: - Medical, Dental, and Vision benefits for you and your family - Life Insurance and Disability Benefits - Retirement Plan (e.g., 401K, pension) - Parental Leave - Fertility and family building benefits through Carrot - Monthly stipend to support your wellness, lifestyle, and work-life balance - Daily lunches and snacks in the office to keep you at your best - Take what you need vacation policy (subject to local requirements; UK employees receive 25 days of statutory leave) These benefits are described in more detail in Decagon’s policies, may vary by location, and can change at any time according to applicable compensation and benefits plans.
Research Engineer, Safety
About Decagon Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences. Our technology enables industry-defining enterprises like Avis Budget Group, Block’s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel. We’re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We’re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others. We’re an in-office company, driven by a shared commitment to excellence and velocity. Our values — Just Get It Done, Invent What Customers Want, Winner’s Mindset, and The Polymath Principle — shape how we work and grow as a team. About the Team Read more about the research team's work here: https://decagon.ai/blog/introducing-decagon-labs The Research team develops the model and decision-making stack that powers Decagon’s conversational agents for enterprise support. We research, adapt, and implement state-of-the-art techniques in model training, prompting, orchestration, and evaluation in order to make our agents more accurate, robust, and efficient in real-world deployments. Our goal is to push the frontier of applied conversational AI: agents that reliably understand nuanced intent, track long context, and take the right actions under uncertainty. We measure success the way customers feel it: higher resolution rates, better user satisfaction, and consistent behavior at scale. About the Role As a Research Engineer focused on Safety, you’ll be responsible for making Decagon’s AI agents safe, reliable, and controllable, from evaluation through production. You’ll identify real-world failure modes and build the models, evaluations, and safeguards that prevent them. We’re looking for strong engineers who want to advance applied AI safety in production. People here own their work end-to-end, ship real improvements, and are trusted to make high-impact technical decisions. In this role, you will - Research and build safeguards against prompt injection, unsafe tool use, sensitive-data disclosure, policy violations, and hallucinated commitments - Build adversarial evaluations, simulations, red-team datasets, and regression suites informed by production failures - Develop and deploy classifiers, judges, reward signals, post-training methods, and runtime safeguards for safer agent behavior - Analyze production traces and incidents to identify root causes, test mitigations, and measure their impact - Partner with Security, Product, Infrastructure, Legal, and customer-facing teams to turn enterprise requirements into scalable safeguards and rollout practices Your background looks something like this - 2+ years of experience in AI/ML engineering, research, or AI safety - Hands-on experience evaluating, post-training, or deploying language models or agentic systems - Experience with modern post-training techniques, such as reinforcement learning, preference optimization, distillation, model routing, and synthetic-data generation - Experience with adversarial testing, model red teaming, prompt injection, policy enforcement, privacy, or safe tool use - Fluency in Python and modern ML tooling, with strong experimental judgment and the engineering depth to ship production systems - Comfort owning ambiguous, high-stakes technical problems and making clear risk and product tradeoffs Even better if you have - Experience building safeguards for high-stakes or regulated enterprise workflows - Familiarity with human-in-the-loop review, incident response, or responsible rollout frameworks for ML systems Compensation $200K – $400K + Offers Equity This range reflects the expected compensation for this role. Compensation within the range is determined based on experience, skills, and the scope of responsibilities, with flexibility for candidates who demonstrate exceptional impact. In addition to base salary, we offer competitive equity. Final compensation may vary based on location within the United States. Benefits We proudly offer the following benefits for our full-time employees: - Medical, Dental, and Vision benefits for you and your family - Life Insurance and Disability Benefits - Retirement Plan (e.g., 401K, pension) - Parental Leave - Fertility and family building benefits through Carrot - Monthly stipend to support your wellness, lifestyle, and work-life balance - Daily lunches and snacks in the office to keep you at your best - Take what you need vacation policy (subject to local requirements; UK employees receive 25 days of statutory leave) These benefits are described in more detail in Decagon’s policies, may vary by location, and can change at any time according to applicable compensation and benefits plans.
AI Engineer – Forward Deployed Engineering (AI FDE...
PLEASE NOTE : Due to federal contract requirements and client site access obligations, U.S. citizenship and eligibility for a U.S. government secret clearance are required to access classified information. The position is based in the Washington, D.C., Maryland, or Virginia metropolitan area and includes periodic on‑site work and client collaboration. Candidates with an active Secret or higher clearance are strongly encouraged to apply. The AI Forward Deployed Engineering (AI FDE) team is a highly specialized customer-facing AI team at Databricks. We deliver professional services engagements to help our customers build and productionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in GenAI, LLMOps, and ML more broadly. This role can be remote. The impact you will have: - Develop cutting-edge GenAI solutions, incorporating the latest techniques from Databricks AI research to solve customer problems - Own production rollouts of consumer and internally facing GenAI applications - Serve as a trusted technical advisor to customers across a variety of domains - Present at conferences such as Data + AI Summit , recognized as a thought leader internally and externally - Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap What we look for: - Experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, etc., with tools such as HuggingFace, LangChain, and DSPy - Expertise in deploying production-grade GenAI applications, including evaluation and optimizations - Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools, i.e. pandas, scikit-learn, PyTorch, etc. - Experience building production-grade machine learning deployments on AWS, Azure, or GCP - Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience - Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike - Passion for collaboration, life-long learning, and driving business value through AI - [Preferred] Experience using the Databricks Intelligence Platform and Apache Spark™ to process large-scale distributed datasets - Willing to travel once every 4-8 weeks to see customers (as needed) Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . Local Pay Range $182,000 - $250,208 USD About Databricks Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
Senior Customer Success Manager, Managed Inference
Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster. We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI. We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services. If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe. About This Role: We are seeking a highly motivated and skilled Senior Customer Success Manager with a strong background in customer engagement and a deep technical understanding of cloud computing, AI, and ML. The ideal candidate has experience supporting customers running production AI inference workloads and understands the operational, technical, and business challenges associated with deploying and scaling AI applications. Experience supporting Managed Inference, model serving platforms, LLM deployments, AI agents, or GPU-based inference environments is highly preferred. This role is pivotal in ensuring that our clients maximize the value of our solutions, guiding them through the technical complexities and empowering them with the tools and knowledge to achieve their business and sustainability goals. This is a full-time position. What You’ll Be Working On: - Customer Relationship Management: Develop and maintain strong, long-lasting customer relationships, understanding their business needs and technical requirements. - Own strategic relationships with AI-native customers deploying production inference workloads, helping them achieve performance, reliability, latency, scalability, and cost optimization objectives. - Customer Advocacy: Work on case studies to highlight customer successes. - Technical Liaison: Act as the bridge between our clients and our technical team, ensuring clear communication and alignment of goals. - Technical Guidance and Support: Provide technical guidance and support to customers in the implementation and optimization of our cloud-based AI and ML solutions including Kubernetes solutions. - Partner with customers on model deployment strategies, inference optimization, GPU utilization, autoscaling, throughput management, observability, and production readiness best practices. - Performance Monitoring and Reporting: Conduct regular reviews and report on customer progress, ensuring the achievement of key performance indicators and return on investment. - Monitor customer adoption, inference consumption trends, latency metrics, uptime, capacity utilization, and business outcomes to identify growth opportunities and potential risks. - Customer Journey Coordination: Coordinate with the product and engineering teams to define customer journeys, challenges, and opportunities. - Partner closely with Product, Engineering, CS, and Solutions teams to advocate for customer needs and influence roadmap priorities related to inference platforms and AI services. - Industry and Technical Awareness: Stay up-to-date with industry trends, technical advancements, and regulatory changes to provide strategic advice to clients. - Maintain a strong understanding of emerging AI technologies, LLMs, model serving architectures, agentic workflows, inference optimization techniques, and GPU infrastructure trends. - Customer Training: Deliver training sessions and workshops to customers on the use and benefits of our products and services. - Issue Resolution: Address and resolve customer concerns, ensuring high levels of customer satisfaction and retention. - Act as a trusted advisor during customer escalations, service incidents, and critical production events, helping customers navigate complex technical and business challenges. What You’ll Bring to the Team: - Educational Background: Bachelor’s degree in Business, Engineering, or a related field. Advanced degree preferred. - Professional Experience: Proven experience in customer success, technical account management, or a similar role in a technology-driven environment. - 3+ years of experience supporting enterprise cloud, AI, machine learning, developer platform, or infrastructure customers. - Experience managing strategic accounts running production AI workloads is strongly preferred. - Technical Proficiency: Strong technical foundation in cloud computing platforms, AI, and ML technologies, with the ability to communicate complex concepts in simple terms. - Strong understanding of inference workloads, model serving architectures, Kubernetes, containers, APIs, GPU infrastructure, and AI application deployment patterns. - Familiarity with LLMs, RAG architectures, agentic applications, model performance metrics, and inference optimization concepts is highly desirable. - Interpersonal Skills: Excellent interpersonal, communication, and presentation skills. - Relationship Building: Demonstrated ability to build relationships at all levels within an organization. - Ability to engage effectively with both executive stakeholders and deeply technical customer teams, including AI Engineers, ML Engineers, Platform Engineers, and Infrastructure leaders. - Adaptability: Comfortable working in a fast-paced environment with ambiguous and/or iterative fact-sets. Benefits: - Competitive compensation and equity packages - Restricted Stock Units - Paid time off, paid holidays & leave of absence programs - Comprehensive health, dental & vision insurance - Employer contributions to HSA account - Paid parental leave - Paid life insurance, short-term and long-term disability - Professional development & tuition reimbursement - Mental health & wellness support - Commuter benefits (parking & transit) - Cell phone stipend - 401(k) Retirement plan with company match up to 4% of salary - Volunteer time off - Global travel insurance & emergency assistance - Daily meals allowance - Additional perks & programs specific to location Compensation Range Compensation will be paid in the range of up to $190,000 – $215,000 OTE. Restricted Stock Units are included in all offers. Compensation to be determined by the applicant's knowledge, education, and abilities, as well as internal equity and alignment with market data. Crusoe is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, disability, genetic information, pregnancy, citizenship, marital status, sex/gender, sexual preference/ orientation, gender identity, age, veteran status, national origin, or any other status protected by law or regulation.
Get tailored job alerts, not another account to manage
Start with your email. We’ll send the newest roles for this site each day; unsubscribe in one click.
Coming soon: automatic applications. You’ll be able to define your criteria and let Jobs in AI apply to matching roles. This is a preview only—applications are not automated today.