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Member of Technical Staff - Data Quality Engineer...
OUR MISSION Reflection is a research lab making intelligence open and accessible for everyone to use, customize, and build on. We build open models that let anyone control their intelligence and help shape the future of AI. Our mission: make intelligence open and accessible to all. ABOUT THE ROLE Data is playing an increasingly crucial role at the frontier of AI innovation. Many of the most meaningful advances in recent years have come not from new architectures, but from better data. As a member of the Data Team, your mission is to ensure that the data used to train and evaluate our models meets a high bar for quality, reliability, and downstream impact. You will directly shape how our models perform on critical capabilities — agentic tool use, long-horizon reasoning and robust safety alignment. Working with world-class researchers on our post-training teams, you’ll help turn fuzzy notions of “good data” into concrete, measurable standards that scale across large data campaigns. We’re looking for engineers who combine strong engineering fundamentals with a deep curiosity about data quality and its impact on model behavior Working closely with our post-training teams you will: - Own upstream data quality for LLM post-training and evaluation by analyzing expert-developed datasets and operationalizing quality standards for reasoning, alignment, and agentic use cases - Partner closely with research and post-training teams to translate requirements into measurable quality signals, and provide actionable feedback to external data vendors - Design, validate, and scale automated QA methods, including LLM-as-a-Judge frameworks, to reliably measure data quality across large campaigns - Build reusable QA pipelines that reliably deliver high-quality data to post-training teams for model training and evaluation - Monitor and report on data quality over time, driving continuous iteration on quality standards, processes, and acceptance criteria ABOUT YOU - Strong engineering fundamentals with experience building data pipelines, QA systems, or evaluation workflows for post-training data and agentic environments - Detail-oriented with an analytical mindset, able to identify failure modes, inconsistencies, and subtle issues that affect data quality - Solid understanding of how data quality impacts training (SFT and RL) and evaluation, with the ability to translate quality concerns into concrete signals, decisions, and feedback - Experience designing and validating automated quality checks, including rule-based systems, statistical methods, or model-assisted approaches such as LLM-as-a-Judge - Comfortable working autonomously, owning problems end-to-end, and collaborating effectively with researchers, engineers, and operations partners SKILLS AND QUALIFICATIONS - Proficiency in Python and building ML / LLM workflows. Must be comfortable debugging and writing scalable code - Experience working with large datasets and automated evaluation or quality-checking systems - Familiarity with how LLMs work and can describe how models are trained and evaluated - Excellent communication skills with the ability to clearly articulate complex technical concepts across teams WHAT WE OFFER: We believe that to make intelligence open and accessible to all, you need to start at the foundation. Joining Reflection means building from the ground up as part of a talent-dense team. You will help define our future as a company, and help define the future of open foundational models. We want you to do the most impactful work of your career with the confidence that you and the people you care about most are supported. - Top-tier compensation: Salary and equity structured to recognize and retain our talent globally. - Stock options: Everyone who joins and contributes to Reflection's success gets to share in the upside through stock options. - Health & wellness: Comprehensive medical, dental, vision, and life, with an annual wellness allowance. - Meals: Lunch and dinner are provided in the office daily. - Life & family: 22 weeks paid parental leave for all new birthing and non-birthing parents, including adoptive and surrogate journeys. - Vacation days: Unlimited paid time off in the U.S. and 30 days in the U.K. - Sponsorship support: We sponsor visas to help exceptional talent join our team and support long-term immigration pathways where applicable. - Team building: We have regular off-sites, happy hours, and team celebrations. Export Control Notice: This position may require access to technology or source code subject to the U.S. Export Administration Regulations. Any offer of employment for this role may be conditioned on the Company's ability to provide the candidate with access to such technology or source code in compliance with applicable U.S. export control laws, which may require the Company to seek government authorization. Candidate and Employee Privacy Notice: We collect and process personal data about applicants for the purposes described in our Candidate and Employee Privacy Notice https://cdn.sanity.io/files/irpbdun9/production/ee08c38bf22bd45a3f3a81f6a7abddb9210726b7.pdf
Research Engineer - RL Infrastructure
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, 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 systems infrastructure behind large-scale RL and distributed 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. WHY THIS ROLE MATTERS The next frontier in AI will not be unlocked by models alone. It will be unlocked by systems that let those models train faster, adapt continuously, and operate across real environments at scale. That infrastructure does not exist yet in the form the world needs. We’re building it. BENEFITS & PERKS - Cash Compensation Range of $150-350k, plus equity. - Flexible work arrangements, with the option to work remotely or in person from our San Francisco office. - Visa sponsorship and relocation support for international candidates. - Quarterly team offsites, hackathons, conferences, and learning opportunities. - A deeply technical, high-agency team working on infrastructure for open superintelligence. If you’re excited about building the systems foundation for frontier-scale RL and open superintelligence, we’d love to hear from you.
Member of Technical Staff - Training Platform
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 You'll help build our hosted training platform - the product that lets users launch LoRA and full fine-tuning runs on managed GPU clusters with a single API call or a few clicks. The role spans the developer-facing platform and the underlying Kubernetes-based training infrastructure that runs the jobs. CORE TECHNICAL RESPONSIBILITIES HOSTED TRAINING INFRASTRUCTURE - Design and operate Kubernetes-based training and inference orchestration across multi-cluster, multi-cloud GPU fleets - Build and maintain Helm charts that compose trainers, inference servers, environment servers, and supporting services into reproducible "Training stacks" - Develop the Python control-plane agents that watch pods, report run state to the platform, and keep clusters in sync - Implement scheduling and autoscaling for heterogeneous hardware (H100/H200/B200) using KEDA, LeaderWorkerSet, taints/tolerations, and gang scheduling - Run a tight GitOps workflow - every change ships through PRs, Helm values, and CI - Build node-local model caches, checkpoint pipelines, and shared storage for fast cold starts - Operate the observability stack (Prometheus, Grafana, Loki, DCGM) and make GPU cluster debugging fast PLATFORM DEVELOPMENT - Build the developer-facing surfaces for hosted training: job submission, live run monitoring, logs, metrics, model/adapter management, comparisons - Develop FastAPI backend services and REST APIs that bridge the platform to running clusters - Build real-time monitoring and debugging tools (streaming logs, step-level metrics, failure analysis) - Ship product UI in Next.js / React / TypeScript with shadcn, Tailwind, tRPC, and TanStack Query RESEARCH BRIDGE - Interface with the RL trainer, inference servers, and environment servers running inside our clusters - Productize new training capabilities (new model architectures, RL algorithms, modes) TECHNICAL REQUIREMENTS We're looking for engineers who are fluent across three areas - you don't need to be the world's best at any one, but you should have real depth in all three and a clear point of view on how they connect. AI & GPU LANDSCAPE - Strong working knowledge of the modern AI stack - open model families, finetuning techniques (LoRA, QLoRA, full FT, RLHF/RLAIF), inference engines (vLLM, SGLang, TensorRT-LLM) - Familiarity with GPU hardware tradeoffs (H100 / H200 / B200, NVLink, interconnects, memory hierarchy) and what they mean for training and inference workloads - Understanding of distributed training fundamentals (data/tensor/pipeline/expert parallelism, NCCL, multi-node scheduling) - Awareness of what's happening at the frontier - new models, training methods, infra patterns - and the ability to translate that into product decisions KUBERNETES & INFRASTRUCTURE - Strong Kubernetes operations experience - Helm, CRDs, operators, KEDA, gang scheduling, GPU operator - Comfortable debugging real production clusters (kubectl, pod lifecycle, node issues, networking) - Cloud platform experience (GCP preferred - GCS, GKE, Cloud Run, Cloud Tasks) - Infrastructure automation (Helm, Terraform, Ansible) and a GitOps mindset - Observability: Prometheus, Grafana, Loki, OpenTelemetry, DCGM - Linux fundamentals: networking, namespaces, performance tuning PROGRAMMING & PLATFORM - Strong Python backend development (FastAPI, async, SQLAlchemy) - Comfortable building Python control-plane agents that talk to Kubernetes APIs - Modern frontend development (TypeScript, React/Next.js, Tailwind, shadcn) - enough to ship product surfaces end-to-end - REST and tRPC API design - Experience building developer tools, dashboards, and live-monitoring UIs WHAT WE OFFER - Cash compensation $150K–$300K with significant equity - Flexible work arrangement (remote or San Francisco office) - Full visa sponsorship and relocation support - Professional development budget for courses and conferences - Regular team off-sites and conference attendance - Opportunity to shape the future of decentralized AI development GROWTH OPPORTUNITY You'll join a team of experienced engineers and researchers working on cutting-edge problems in AI infrastructure. We believe in open development and encourage team members to contribute to the broader AI community through research and open-source work. We value potential over perfection - if you're passionate about democratizing AI development and have experience in either platform or infrastructure development (ideally both), we want to talk to you. Ready to help shape the future of AI? Apply now and join us in our mission to make powerful AI models accessible to everyone.
Member of Technical Staff - Inference
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 hybrid position spanning cloud LLM serving, LLM inference optimization and RL systems. You will be working on advancing our ability to evaluate and serve models trained with our RL Lab at scale. The two key areas are: 1. Building the infrastructure to serve LLMs efficiently at scale. 2. Optimization and integration of inference systems into our RL training stack. CORE TECHNICAL RESPONSIBILITIES LLM Serving - Multi‑tenant LLM Serving: Build a multi-tenant LLM serving platform that operates across our cloud GPU fleets. - GPU‑Aware Scheduling: Design placement and scheduling algorithms for heterogeneous accelerators. - Resilience & Failover: Implement multi‑region/zone failover and traffic shifting for resilience and cost control. - Autoscaling & Routing: Build autoscaling, routing, and load balancing to meet throughput/latency SLOs. - Model Distribution: Optimize model distribution and cold-start times across clusters. Inference Optimization & Performance - Framework Development: Integrate and contribute to LLM inference frameworks such as vLLM, SGLang, TensorRT‑LLM. - Parallelism and Configuration Tuning: Optimize configurations for tensor/pipeline/expert parallelism, prefix caching, memory management and other axes for maximum performance. - End‑to‑End Performance: Profile kernels, memory bandwidth and transport; apply techniques such as quantization and speculative decoding. - Perf Suites: Develop reproducible performance suites (latency, throughput, context length, batch size, precision). - RL Integration: Embed and optimize distributed inference within our RL stack. Platform & Tooling - CI/CD: Establish CI/CD with artifact promotion, performance gates, and reproducible builds. - Observability: Build metrics, logs, tracing; structured incident response and SLO management. - Docs & Collaboration: Document architectures, playbooks, and API contracts; mentor and collaborate cross‑functionally. TECHNICAL REQUIREMENTS Required Experience - Building ML Systems at Scale: 3+ years building and running large‑scale ML/LLM services with clear latency/availability SLOs. - Inference Backends: Hands‑on with at least one of vLLM, SGLang, TensorRT‑LLM. - Distributed Serving Infra: Familiarity with distributed and disaggregated serving infrastructure such as NVIDIA Dynamo. - Inference Internals: Deep understanding of prefill vs. decode, KV‑cache behavior, batching, sampling, speculative decoding, parallelism strategies. - Full‑Stack Debugging: Comfortable debugging CUDA/NCCL, drivers/kernels, containers, service mesh/networking, and storage, owning incidents end‑to‑end. Infrastructure Skills - Python: Systems tooling and backend services. - PyTorch: LLM Inference engine development and integration, deployment readiness. - Cloud & Automation: AWS/GCP service experience, cloud deployment patterns. - Kubernetes: Running infrastructure at scale with containers on Kubernetes. - GPU & Networking: Architecture, CUDA runtime, NCCL, InfiniBand; GPU‑aware bin‑packing and scheduling across heterogeneous fleets. Nice to Have - Kernel‑Level Optimization: Familiarity with CUDA/Triton kernel development; Nsight Systems/Compute profiling. - Systems Performance Languages: Rust, C++. - Data & Observability: Kafka/PubSub, Redis, gRPC/Protobuf; Prometheus/Grafana, OpenTelemetry; reliability patterns. - Infra & Config Automation: Terraform/Ansible, infrastructure-as-code, reproducible environments - Open Source: Contributions to serving, inference, or RL infrastructure projects. WHAT WE OFFER - Cash Compensation Range of $150-300k with significant equity incentives - Flexible work arrangement (remote or San Francisco office) - Full visa sponsorship and relocation support - Professional development budget - Regular team off-sites and conference attendance - Opportunity to shape decentralized AI and RL at Prime Intellect GROWTH OPPORTUNITY You'll join a team of experienced engineers and researchers working on cutting-edge problems in AI infrastructure. We believe in open development and encourage team members to contribute to the broader AI community through research and open-source contributions. We value potential over perfection. If you're passionate about democratizing AI development, we want to talk to you. Ready to help shape the future of AI? Apply now and join us in our mission to make powerful AI models accessible to everyone.
Research Intern (BS/MS/PhD)
About the Role At Pika, we are shaping the future of creative infrastructure with real-time, multimodal generation and intelligent agentic platforms. We are looking for passionate Research Interns at the BS, MS, or PhD level with significant experience working on Videogen to join our team and make an immediate impact. In this role, you will directly contribute to mid-training and post-training efforts for Videogen models and support advanced generative systems. Familiarity with multimodal large language models (MLLMs) is valued as a bonus. This internship is ideal for students and researchers who have hands-on Videogen experience, are excited to work at the intersection of generative AI and multimedia, and are eager to make meaningful contributions alongside top AI talent. Flexible part-time and full-time internship terms are available. What You’ll Do - Work directly on large-scale mid-training, post-training, and optimization efforts for Videogen models. - Design, prototype, and refine algorithms for high-throughput video and multimedia data pipelines. - Collaborate with experienced researchers and engineers on advanced AI and video generation technology. - Apply new research techniques and production-scale workflows to deploy Videogen advancements into real-world applications. What We’re Looking For - BS, MS, or PhD-level backgrounds, with demonstrated Videogen experience strongly preferred. - Expertise in video generation models or frameworks. - Proficiency in Python and ML toolkits (such as PyTorch, TensorFlow, etc.). - Prior exposure to multimodal LLM inference, generative models, or related areas is a bonus. - Fast learner who can adapt to new research and technologies quickly. - Excellent communicator and effective team collaborator. What We Offer - Flexible internship: part-time or full-time roles available - Unique opportunity to make an immediate impact with Videogen and generative AI research - Mentorship and collaboration with innovative researchers and engineers - Competitive compensation and a dynamic, mission-driven environment - Onsite work setup, with headquarters in Palo Alto, CA About Pika Pika empowers creators by building cutting-edge agentic and multimedia platforms. Our mission is to remove technical barriers and make real-time generative and intelligent orchestration accessible to everyone. If you are ready to accelerate your AI career, hone your video generation expertise, and contribute to the next wave of creative technology, we want to hear from you.
ML Engineer, Inference & Optimization
ABOUT THE ROLE We are seeking Senior/Staff level Inference Engineers to accelerate the performance of Pika's AI-driven products. In this highly technical role, you will operate at the intersection of cutting-edge inference acceleration, GPU parallelism, advanced model deployment, and video generation technologies. Your expertise will drive significant improvements to model speed and efficiency, ensuring our creative AI systems deliver industry-leading user experiences at scale. You will design and optimize inference pipelines, implement state-of-the-art acceleration techniques, and work closely with researchers and engineers across the team to push the boundaries of what’s possible in real-time AI deployment. Your efforts will play a foundational role in powering the next generation of Pika’s video and language models. WHAT YOU’LL DO - Accelerate Inference: Lead and implement advanced inference acceleration techniques, including attention optimization and quantization for efficient model serving. - Maximize GPU Parallelism: Engineer and optimize GPU strategies across tensor, sequence, and pipeline parallelism (TP, SP, PP) for maximal efficiency and scalability. - Programming for Performance: Develop and optimize high-performance computing kernels and distributed workloads using CUDA and NCCL. - Advance AI Deployment: Collaborate with research and engineering teams to bring state-of-the-art videogen and large language models into production. - Improve Training Efficiency: (Bonus) Contribute to improvements in model training speed, stability, and resource utilization as part of our deployment lifecycle. - Technical Excellence: Drive rigorous code reviews, participate in technical discussions, and mentor fellow engineers on best practices in inference and GPU programming. WHAT WE’RE LOOKING FOR - Experience: 5+ years engineering experience, with a strong track record in inference acceleration and model deployment at scale. - Inference Mastery: Proven expertise in inference optimization, including quantization, attention acceleration, and deep learning compiler stacks. - GPU & Parallelism: Deep knowledge of GPU programming (CUDA, NCCL) and experience with SP, TP, PP, and other forms of parallelism for distributed inference. - AI Domain Knowledge: Familiarity with video generation (videogen) models and large language models (LLMs). - Collaboration: Strong cross-discipline communication skills; able to drive shared goals across research and engineering functions. - Ownership Mindset: Self-driven, solutions-oriented, and capable of managing ambiguity in a fast-paced startup environment. - Bonus: Experience in enhancing training efficiency, stability, or resource optimization for large models. NICE TO HAVE - Experience with high-throughput video or real-time streaming model deployment - Familiarity with distributed training and optimization toolkits - Contributions to open source projects in AI infrastructure or deep learning compilers - Startup or rapid prototyping experience WHAT WE OFFER - Competitive salary in the AI industry - Equity in a fast-growing startup shaping the future of AI - Comprehensive health benefits, monthly stipends, company retreats - A supportive and collaborative office culture—we’re all building and launching together ABOUT PIKA At Pika, we're crafting a future where video creation is seamless, intuitive, and universally accessible. Our mission is to empower creativity by breaking down technical barriers using the transformative power of AI. We’re a tight-knit, energetic team based in Palo Alto, CA, valuing efficiency, curiosity, and the ambition to make a meaningful impact on the world. We work from our Palo Alto office 3–5 days a week and welcome applicants who are eager to contribute onsite.
Principal Machine Learning 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. Who We're Looking For As a Principal Machine Learning Engineer in Delivery, you are an experienced problem solver and technical leader who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple industries, lead technical initiatives, and excel at working directly with customers (and often side-by-side with them on-site) to embed cutting-edge AI models into tools that are useful and used. You’ve shipped ML systems end-to-end and at scale: you design, build and test reliable, scalable ML data pipelines; you know how to explore and manipulate 3D point-cloud and mesh data to enable geometry-aware modelling; you select the right libraries, frameworks and tools and make pragmatic product decisions that set Delivery up for success. Working at the intersection of data science and software engineering, you translate R&D and project outputs into reusable libraries, tooling and products. With at least 5 years industry experience (post Masters or PhD) in a commercial, non-research environment, you're ready to not only execute but also lead and mentor others. You're truly excited about taking ownership of complex work streams and guiding teams to success, while continuously improving the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers. This Role As a Principal MLE, you'll work closely with our Data Scientists, Simulation Engineers, and customers to understand and define the engineering and physics challenges we are solving. You will iterate with customers and use your influence to drive decisions around reliable deployment with measurable outcomes. You'll: - Own the deployment of ML models and engineering surrogates (e.g., deep learning on CAE/CFD/FEA data, time‑series forecasting, anomaly detection, optimization & control) to customer production environments. - Communicate results and trade‑offs to senior stakeholders; steer roadmaps and influence product direction with evidence. - Lead scoping and architecture design for data/ML systems; define success metrics, delivery plans and quality bars. - Excel at building robust and scalable ML systems, training and inference pipelines and APIs, running both on cloud and on-prem environments. The tech stack you will use for this includes: Python, PyTorch, Pandas, fastAPI, Scipy, Kubeflow, among others. - Mentor and develop engineers and data scientists; provide technical direction and clear, calm decision‑making under pressure. - Travel to customer sites in North America, Europe, Asia, Oceania, for an average of 3-4 weeks per quarter, where you'll collaborate closely with customers to build solutions on-site. - Own the scoping of new projects and work-streams with existing customers and taking part in bringing new customers to PhysicsX. As a senior member of the team, you’ll significantly influence our technical direction and will be involved in shaping future solutions and products, while developing your skills as a technical leader. Our delivery teams drive innovation to turn AI models into practical solutions - read our blog to learn more about how you’ll contribute to this exciting journey! 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/Research Engineer, Materials Da...
About Periodic Labs We're an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what's scientifically possible. About the Role At Periodic Labs, we are automating scientific research in materials discovery; the data we collect, and how we represent it, is what makes this possible. We are hiring a materials scientist to work across the entire process: to find where our agents and data fail, and to fix those failures at the source. This is a hybrid research and infrastructure role. Roughly half your time will be spent working directly with our experimental and computational scientists, building agents and tools that solve active research problems. The other half will be spent improving the data those agents depend on. This role requires domain depth, not just data engineering skill. You need to know, for example, what metadata actually matters for a given characterization technique. The research half of the role keeps you grounded in the problems we are actually using LLMs to solve, so the schemas you build serve real research rather than an abstraction of it. The work demands attention to detail and a willingness to get into the weeds: to meticulously read, understand, and improve our data. You'll be as much a materials scientist doing research as the person who makes our agentic harness actually work. What You'll Do - Work directly with lab scientists and the computational team on active research problems, staying close to the actual bottlenecks LLM agents are meant to solve. - Investigate agent traces to identify data errors and agentic failure modes, and eliminate them at the source. - Restructure and re-architect our materials databases (lab experiments, characterization data, computations) around how LLM agents actually reason and fail, not just around human readability. Draw on domain expertise about how to represent the data and what metadata matters. - Work with the hardware and automation teams to make data collection more robust. - Build research software for lab environments, translating scientific requirements into working tools. - Distill what you learn into evaluations that measure agent performance. Mechanics - Minimum experience: 4+ years of research or industry experience in experimental or computational materials science and working with data at scale. - Minimum education: PhD in Materials Science, Chemistry, or a related field, or equivalent industry experience. - Location: Menlo Park, CA - Compensation: $250,000-350,000 + equity - Visa sponsorship: Yes, we sponsor visas.
Research Scientist, Data
ABOUT PERIODIC LABS The most important scientific discoveries of our time won’t happen in a traditional lab. We’re an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what’s scientifically possible. ABOUT THE ROLE You will work on the most important aspect of Scientific AI creation: evaluations and data. This means constructing cutting-edge evaluations based on advanced scientific use cases, sourcing and procuring external datasets, integrating internally generated experimental data into the training stack, constructing training environments for RL. You’ll ensure that the team always has the right assets, in the right shape, to evaluate and improve AI models. You will work with computational and experimental scientists to translate complex scientific workflows into rigorous evaluations and agentic benchmarks, and partner with pretraining, midtraining, and reinforcement learning researchers to identify the data models needed, then build the datasets, environments, and pipelines to deliver it. Your goal will be to create a tight feedback loop between scientific use cases, model evaluation, and training data. WHAT YOU’LL DO - Own the evaluation and data strategy across the training stack, identifying capability gaps and shaping the roadmap with leads of physical science and AI research - Work with domain experts to translate advanced scientific workflows into rigorous evals, benchmarks, and RL environments - Source, evaluate, and procure external datasets across chemistry, physics, materials science, mathematics, simulations, and laboratory instrumentation - Build robust pipelines to ingest, clean, and transform for training large-scale datasets from heterogeneous sources - Build tooling and analysis workflows that help researchers inspect data, understand model failures, and determine which evaluations or datasets to develop next YOU WILL THRIVE IN THIS ROLE IF YOU HAVE - Designed evaluations, benchmarks, or RL environments for language models, agents, or scientific AI systems - Built large-scale data pipelines for LLM pretraining, midtraining, post-training, or evaluation - Strong judgment about dataset and evaluation quality, including scientific relevance, coverage, provenance, licensing, and contamination risks - Strong software and data engineering skills, including familiarity with data processing at scale, dataset versioning, lineage tracking - A research-oriented mindset: you form hypotheses about data, run controlled experiments, measure model outcomes, and iterate with rigor - Research experience in areas such as materials science, solid state chemistry, chemistry, computational physics, semiconductors MECHANICS Minimum education: Bachelor’s degree or similar experience Location: Menlo Park, CA or Montreal, Canada Compensation: $250,000-350,000 + equity Visa sponsorship: Yes, we sponsor visas.
ML Systems Engineer
ABOUT PERIODIC LABS We're an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what's scientifically possible. ABOUT THE ROLE You’ll work alongside some of the world’s leading ML systems engineers, including leaders behind Megatron-LM, SGLang, Liger Kernel, TorchRec, CleanRL, TorchRL, and JAX-MD. We’re looking for exceptional ML Systems Engineers to build the agentic infrastructure powering our large-scale training, inference, and reinforcement learning. You’ll own critical pieces of the ML systems stack to maximize performance, scalability, reliability, and productivity for both engineers and AI agents. WHAT YOU'LL DO - Build and optimize large-scale training and reinforcement learning infrastructure while ensuring its correctness - Develop high-performance inference and serving systems - Design distributed runtimes and scheduling systems for complex ML workloads - Build secure and large-scale sandboxing and execution environments - Optimize memory, GPU kernels and communication for maximum throughput and end-to-end efficiency - Improve scalability, reliability, and efficiency across the ML systems stack WHAT WE'RE LOOKING FOR - Strong systems programming and performance engineering skills - Experience building high-performance ML infrastructure at scale - Ability to own complex technical problems end-to-end - Strong coding ability and engineering judgment, including the ability to work effectively with AI agents to design, implement, test, and debug complex systems - High ownership, fast execution, and a passion for pushing the frontier of AI systems and accelerating scientific discovery YOU SHOULD HAVE DEEP EXPERTISE IN AT LEAST ONE OF THE FOLLOWING: - Training: Strong experience building, debugging and optimizing large-scale training systems with Megatron-LM. Familiarity with TorchTitan, FSDP, veRL, Slime, or other distributed training systems is a plus. - Distributed Runtime: Strong experience with Ray. Familiarity with Monarch or other distributed execution frameworks is a plus. - Inference: Strong experience with SGLang. Familiarity with vLLM, TensorRT-LLM, or production LLM serving systems is a plus. - Sandboxing: Strong experience with secure execution environments, containers, virtualization, or code sandboxing. - GPU Kernels: Strong experience with CUDA, Triton, CUTLASS, CuTe, or custom GPU kernel development. - GPU Communication: Strong experience with NCCL, NVLink, InfiniBand, RDMA, GPUDirect RDMA, or large-scale communication optimization. MECHANICS Minimum education: Bachelor’s degree or similar experience Location: Menlo Park, CA Compensation: $250,000-$350,000 base + equity Visa sponsorship: Yes, we sponsor visas. We’re building a team of the world’s best — the scientists, engineers, and problem-solvers who don’t just follow the frontier, they define it. If you’re driven to bring AI to life in the physical world and make discoveries that have never been made before, you belong here.
Machine Learning Engineer III (Applied Research &...
PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials. Rigorous science and careful analysis is critical to the success of everything we do. Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes. The Opportunity We are seeking Machine Learning Engineers (Applied Research & Model Development) to tackle unique machine learning challenges to advance medicine and improve patient care. You will work closely with teams across biomedical data science, product development, translational research, MLOps, and platform engineering to develop and deploy machine learning models for our AI products and services. You will have the opportunity to work in a company where all employees put patients first. We believe that every team member provides valuable contributions to our success, and no task is too small for anyone if it's important to our company goals. Every PathAI employee is a contributor to our mission to pioneer better patient care by providing the best, most innovative AI tools to biotech, pathologists, clinicians and healthcare organizations. You will work alongside and with leading innovators in the field of AI and medicine and you will play a critical role in product development to impact patient outcomes. - You will design, develop, and deploy machine learning models for research and product development projects. - You will collaborate cross-functionally with scientists, engineers, and product teams to translate biological and clinical requirements into scalable ML solutions. - You will contribute to experimental design and analysis, including ideation, documentation, and reporting. - You will participate in knowledge sharing and team initiatives (e.g., design reviews, journal clubs, ML best practices, governance activities). - You will improve ML pipelines and infrastructure in partnership with MLOps and platform teams. - You will publish and present scientific work, supporting abstracts, manuscripts, and conference contributions. Who You Are: (Required) - You have a Master’s degree in Machine Learning, Computer Science, Data Science, Statistics, Applied Mathematics (or a closely related discipline) with 5 or more years of experience; or a PhD Machine Learning, Computer Science, Data Science, Statistics, Applied Mathematics (or a closely related discipline) with 3 or more years of experience. - You have a proven track record of developing and deploying machine learning models into production or research applications. - You are highly proficient in Python, ML frameworks, and data pipeline development. - You have demonstrated the ability to work independently, lead/contribute to experimental design, and improve ML workflows. - You have demonstrated the ability to collaborate across scientific and engineering teams. Preferred: - You have strong interpersonal and communication skills, with the ability to explain technical concepts to non-experts and thrive in ambiguous environments. - You have excellent organizational skills and the ability to prioritize and manage multiple projects efficiently. This position is based in Boston, NYC or remote. Relocation benefits are not available for this position. The expected salary range for this position based on the primary location Boston, MA is $130,500 - $200,100. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. PathAI is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
Machine Learning Engineer II (Applied Research & M...
PathAI's mission is to improve patient outcomes with AI-powered pathology. Our platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine learning and artificial intelligence. We have a track record of success in deploying AI algorithms for histopathology in translational research, pathology labs and clinical trials. Rigorous science and careful analysis is critical to the success of everything we do. Our team, composed of diverse employees with a wide range of backgrounds and experiences, is passionate about solving challenging problems and making a huge impact on patient outcomes. The Opportunity We are seeking Machine Learning Engineers (Applied Research & Model Development) to tackle unique machine learning challenges to advance medicine and improve patient care. You will work closely with teams across biomedical data science, product development, translational research, MLOps, and platform engineering to develop and deploy machine learning models for our AI products and services. You will have the opportunity to work in a company where all employees put patients first. We believe that every team member provides valuable contributions to our success, and no task is too small for anyone if it's important to our company goals. Every PathAI employee is a contributor to our mission to pioneer better patient care by providing the best, most innovative AI tools to biotech, pathologists, clinicians and healthcare organizations. You will work alongside and with leading innovators in the field of AI and medicine and you will play a critical role in product development to impact patient outcomes. - You will design, develop, and deploy machine learning models for research and product development projects. - You will collaborate cross-functionally with scientists, engineers, and product teams to translate biological and clinical requirements into scalable ML solutions. - You will contribute to experimental design and analysis, including ideation, documentation, and reporting. - You will participate in knowledge sharing and team initiatives (e.g., design reviews, journal clubs, ML best practices, governance activities). - You will improve ML pipelines and infrastructure in partnership with MLOps and platform teams. - You will publish and present scientific work, supporting abstracts, manuscripts, and conference contributions. Who You Are: (Required) - You have a Master’s degree in Machine Learning, Computer Science, Data Science, Statistics, Applied Mathematics (or a closely related discipline) plus 2–4 years of experience, or a PhD Machine Learning, Computer Science, Data Science, Statistics, Applied Mathematics (or a closely related discipline) with 0–2 years of experience. - You have a proven track record of developing and deploying machine learning models into production or research applications. - You are highly proficient in Python, ML frameworks, and data pipeline development. - You have demonstrated the ability to contribute to experimental design, and improve ML workflows. - You have demonstrated the ability to collaborate across scientific and engineering teams. Preferred: - You have the ability to work independently. - You have strong interpersonal and communication skills, with the ability to explain technical concepts to non-experts and thrive in ambiguous environments. - You have excellent organizational skills and the ability to prioritize and manage multiple projects efficiently. This position is based in Boston, NYC or remote. Relocation benefits are not available for this position. The expected salary range for this position based on the primary location Boston, MA is $107,250 - $177,000. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. PathAI is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws.
Solutions Architect - Financial Services
NVIDIA is leading company of AI computing. At NVIDIA, our employees are passionate about AI, HPC , VISUAL, GAMING. Our SA team is more focusing to bring NVIDIA new technology into difference industries. We help to design the architecture of AI computing platform, analysis the AI and HPC applications to deliver our value to customers. You will work closely with industry sales, developer relationship managers and product teams in the hiring position. What You’ll Be Doing: Conduct in-depth analysis of customers' latest needs and co-develop accelerated computing solutions with key customers. Assist in supporting industry accounts and driving research/influencing/new business in those accounts. Deliver technical projects, demos and client support tasks as directed by the Solution Architecture leadership team. Understand and analyze financial customers' workloads and demands for accelerated computing, including but not limited to: quant algorithms, portfolio optimization solving, trading algorithms, LLM training/inference acceleration and optimization, application optimization for Agent AI/RAG, kernel analysis, etc. Assist Top financial customers in onboarding NVIDIA's software and hardware products and solutions, including but not limited to: CUDA, CUDA-X, and our libraries etc. Be an industry thought leader on integrating NVIDIA technology into applications built on Deep Learning, High Performance Data Analytics, Agentic AI and other key applications. Be an internal champion for Data Analytics, Machine Learning, and Deep Learning among the NVIDIA technical community. What We Need To See: 3+ years’ experience with research/development/application of Machine Learning, data analytics, or HPC work flows. Outstanding verbal and written communication skills Ability to work independently with minimal day-to-day direction Knowledge of industry application hotspots and trends in AI and large models for financial field. Familiarity with financial technology stacks and common quant workflow optimization methods. C/C++/Python programming experience Desire to be involved in multiple diverse and innovative projects Experience using scale-out cloud and/or HPC architectures for parallel programming MS or PhD in Engineering, Mathematics, Physics, Computer Science, Data Science, Neuroscience, Experimental Psychology or equivalent experience. Ways To Stand Out From The Crowd: Be familiar with algorithm trading pipeline, including data processing, prediction, portfolio optimization, and execution. LLM/Agent/Harness experience in financial field experience Engineering experience in areas such as model acceleration and kernel optimization. Extensive experience in designing and deploying large scale HPC and enterprise computing systems.
Senior Software Engineer, GNN
NVIDIA is seeking a highly experienced and passionate Senior Software Engineer to join a team building large-scale machine learning solutions, including Graph Neural Networks, Tabular Foundation Models, and ensemble models. This role is critical to accelerating PyTorch-based frameworks and supporting user-facing tools that power NVIDIA’s cutting-edge data science solutions. The team works at the intersection of high-performance computing, GPU acceleration, machine learning infrastructure, and customer-facing software. This is an opportunity to shape efficient training and inference workflows for advanced machine learning models running on NVIDIA GPU infrastructure. If you are passionate about building high-performance software and enabling customers to solve complex data science problems at scale, we would love to hear from you. NVIDIA teams work on some of the world’s most ambitious computing problems, helping organizations adopt AI technologies that enable faster, smarter decisions. What you’ll be doing Develop accelerated, PyTorch-based solutions for large-scale machine learning models, including GNNs, TFMs, and ensemble models, with a focus on efficient training and inference on GPU infrastructure Support CUDA-X Libraries and integrations used in PyTorch-based, large-scale machine learning workflows Partner with developers, product managers, and scientists to develop innovative GNN models and GPU-accelerated implementations for model development and prediction phases Develop solutions that help customers adopt NVIDIA hardware and software, and gather technical requirements directly from customers and Solutions Architects to guide product and engineering priorities Provide technical leadership and mentorship to engineers across the team Identify opportunities to improve the codebase and reduce code-maintenance overhead through re-architecture Apply agentic coding tools to identify and fix bugs, implement new features, and refactor code Solve complex technical issues, explain solutions clearly, exercise technical leadership, and coordinate across multiple teams to achieve shared objectives What we need to see: Bachelor’s degree (or equivalent experience) plus 5 or more years of relevant experience in large-scale machine learning, deep learning, and general data science; or a Master’s degree or PhD plus 3 or more years of relevant experience 3 or more years of experience with PyTorch 2 or more years of experience training enterprise-scale machine learning models across distributed infrastructure 2 or more years of experience designing and operating efficient training and inference workflows on GPU infrastructure, including profiling, scaling, orchestration, and resource utilization Excellent C++ programming and software design skills Proven experience developing, debugging, and optimizing high-performance applications, preferably with GPU acceleration using CUDA Strong collaboration, communication, and documentation habits Ways to stand out from the crowd: Experience developing or deploying Graph Neural Network solutions using PyTorch Geometric, or a similar framework Experience working with data warehouse and lakehouse platforms, such as Snowflake or Databricks Experience in two or more of the following domains: finance, cybersecurity, government or national laboratories, and retail Strong understanding of system architecture, CPU, GPU, memory, and storage systems, as well as performance optimization Experience with customer engagement and technical support, particularly for data science workflows and with vector search and storage solutions, such as FAISS or Milvus NVIDIA is widely considered one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working with us. If you are creative and autonomous, we want to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 19, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Senior Integration Engineer, End-to-End Model - Au...
Intelligent machines powered by artificial intelligence are transforming transportation. NVIDIA is building the computing platforms, software, and AI systems that enable autonomous vehicles to perceive, reason, and act in complex environments. Our team develops NVIDIA’s end-to-end autonomous driving application. We are looking for a Senior Integration Engineer to accelerate the development, integration, evaluation, and deployment of end-to-end driving models across large-scale training infrastructure, simulation environments, and production vehicle platforms. In this role, you will work across model development, data, simulation, systems software, and vehicle engineering. You will help turn rapidly evolving AI models into reliable, high-performance autonomous driving functionality running on NVIDIA’s heterogeneous computing platforms. What you’ll be doing: Integrate learned driving models with vehicle interfaces, sensor inputs, localization, mapping, safety systems, and other autonomous driving components. Establish clear model input, output, timing, state-management, and runtime interface contracts. Partner with model developers to improve model quality, debuggability, runtime behavior, and readiness for deployment. Investigate discrepancies between model behavior in development environments and on target vehicle platforms. Optimize model inference and surrounding software to meet latency, throughput, memory, determinism, and power requirements. Develop tools and metrics for evaluating driving quality, safety, robustness, and regression performance at scale. Perform in-vehicle testing, collect and analyze driving data, and complete autonomous driving missions. Develop high-quality production code in C++ and Python using CUDA and other GPU-accelerated technologies. What we need to see: PhD with 1+ year, MS with 3+ years, or BS (or equivalent experience) with 5+ years of relevant experience in Computer Science, Computer Engineering, Robotics, Machine Learning, or a related field. Strong C++ programming, software architecture, debugging, and performance-analysis skills, and model inference technologies such as CUDA and TensorRT. Proficiency in Python and experience working with modern machine-learning frameworks such as PyTorch. Experience developing software on Linux and embedded or real-time operating systems such as QNX. Experience integrating machine-learning models into complex, performance-sensitive production systems. Ability to diagnose issues across model behavior, application software, middleware, operating systems, and hardware. Experience with autonomous driving, robotics, ADAS, or another real-time intelligent system. Ways to stand out from the crowd: Experience deploying end-to-end driving, robotics, or embodied-AI models on production hardware. Familiarity with model optimization, quantization, compilation, profiling, and hardware-aware neural-network design. A track record of turning research models into robust, measurable, and maintainable product functionality. Self-motivation, sound engineering judgment, and a passion for solving cross-functional integration challenges. #AutonomousVehicles Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 22, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Senior HPC Performance Engineer - AI for Science a...
NVIDIA has become the platform upon which every new AI-powered application is built. We are seeking a Sr. HPC Performance engineer to join our team of scientists and engineers passionate about building the next generation of scientific machine learning (ML) frameworks. Starting with digital biology, through high performance computing (HPC) and powerful ML methods, together, we will advance NVIDIA’s capacity to accelerate AI for Science and industries that depend on it. What you'll be doing: Design and implement computationally performant features for large scale, CUDA-backed ML training frameworks, using low level acceleration and scaling strategies such as kernel design, GPU porting, data structure innovations, distributed learning technologies Optimize computational performance of wide range of business-critical ML models via accelerated hardware and software stack, as well as algorithmic improvements Develop and maintain HPC software stack for atomistic modeling and generative machine learning in digital biology and beyond Collaborate with multiple HPC, AI infrastructure, and research teams Drive the testing and maintenance of the algorithms and software modules What we need to see: Advanced degree in a quantitative field such as Computer Science, Computational Biophysics, Computational Chemistry, Physics, Mathematics, or equivalent experience 5+ years of relevant experience Consistent track record in performance engineering as well as software design, building and packaging and launching software products, with a focus on acceleration Deep understanding of parallel programming in C++, Python; programming experience CUDA or OAI Triton Fluent in modern machine learning frameworks such as PyTorch, JAX, Warp Experience with HPC solutions to research problems for biology or chemistry, including but not limited to atomistic simulations Recognized for technical leadership contributions, capable of self-direction, and ability to learn from and teach others You should display strong communication skills, be organized and self-motivated, and play well with others (be an excellent teammate!) Ways to stand out from the crowd: Contribution to major scientific AI for Science codebase with acceleration features such as new kernels Familiarity with pioneering language and geometric models used in AI for Science applications in biology and chemistry With highly competitive salaries and a comprehensive benefits package, NVIDIA is widely considered to be one of the technology industry's most desirable employers. We are an equal opportunity employer and value diversity at our company. We have some of the most forward-thinking, resourceful and talented people in the world working with us and our engineering teams are growing fast in some of the hottest state-of-the-art fields: Digital Biology, Artificial Intelligence, and Autonomous Vehicles. Are you a creative and autonomous engineer with a real passion for machine learning, computational chemistry, data science & parallel computing? If so, we want to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until February 21, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
PhD Research Intern, Embodied and Agentic AI - 202...
NVIDIA is searching for several interns to join the Learning and Perception research team for an internship in the areas of embodied and agentic AI, such as perception, agentic reasoning, multi-modal and continual learning, AI scientists, and so on. We are passionate about research that pushes boundaries but also has impact in the real world. You will work within an amazing and collaborative research team that has a long history of publishing the results of internship projects at top venues in computer vision and machine learning. We have many experienced mentors in the team, and our interns often choose to return for another internship. We carefully craft internship projects such that they match the interns' expertise. What you'll be doing: Research, design, and implement novel embodied and agentic AI methods. Work collaboratively with your mentor and a broader team of world-class researchers to develop your ideas from early hypothesis through rigorous experimentation. Partner with product and engineering groups to transfer research breakthroughs into real-world applications Rapidly prototype and iterate on ideas using state-of-the-art hardware and infrastructure. What we need to see: Must be pursuing a PhD in Computer Science/Engineering, Electrical Engineering, etc. Excellent knowledge of theory and practice of AI, in particular in computer vision, LLMs, and agents. Excellent programming skills in some rapid prototyping environment such as Python, C++. Knowledge of common machine learning frameworks, such as PyTorch. Promising research track record. Excellent communications skills. Ways to stand out from the crowd: Knowledge in parallel programming (e.g., CUDA) is a plus. NVIDIA is widely considered to be one of the technology world’s most desirable employers with competitive salaries and a generous benefits package. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our best-in-class engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for computer architecture and technology, we want to hear from you! Our internship hourly rates are a standard pay based on the position, your location, year in school, degree, and experience. The expected hourly rates for this position are: US, CA, Santa Clara: 38 USD – 94 USD. US, CA, Remote: 30 USD – 94 USD. You will also be eligible for Intern benefits . Applications for this job will be accepted at least until November 3, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.
Senior/Staff Software Engineer, ML 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 Role The Autonomy ML Infrastructure team is responsible for building & improving the core infrastructure for autonomy teams at Nuro. In this role, you will work closely with teams across Nuro, to design, build and deploy core infrastructure components in machine learning model life cycle, to push the autonomous future forward. You will have an opportunity to work across the full stack of machine learning solutions - from designing robust and scalable model pipelines to building to deploying the optimized models on Nuro’s fleet of self-driving robots! About the Work - Optimize Nuro’s autonomy stack with cutting-edge optimization techniques like quantization, distillation, and model compression. - Work with autonomy engineers to optimize, validate, and deploy large language models. - Develop and maintain a world-class model compiler framework, FTL . - Write robust, high quality software to increase our confidence in our vehicle’s ability to navigate safely on-road. - Collaborate closely with machine learning domain experts and engineers across behavior, perception and mapping to design and implement end-to-end learned ML solutions. About You - 3+ years of relevant experience in ML optimization infrastructure. - Experience with ML optimization techniques such as quantization and pruning, and ML compilers. - Experience maintaining, profiling, and optimizing GPU ML compilers & runtimes. - Proficient in Python and working experience with C++ and CUDA. - Working experience deep learning frameworks (like PyTorch, Jax, Tensorflow, Keras). - Proficient in Python and working experience with C++. - You are passionate about accelerating the benefits of robotics for everyday life. 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. #LI-DNP
Software Engineer, Agents
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 This role focuses on designing and building agentic systems that power creative workflows for short-form video. You'll help shape how AI agents reduce friction, accelerate learning, and unlock new creative possibilities for our users — building agents that can take creative actions on a user's behalf — while experimenting with emerging models and techniques to bring these experiences to life in production. Software Engineers on the agents team work across product, machine-learning, and systems engineering — owning meaningful problems end-to-end and helping define how our agents are built and deployed across the product. While we don’t use hierarchical titles or levels internally, we are currently hiring engineers who have operated at senior or staff+ scope. Responsibilities - Build and ship end-to-end agentic systems that meaningfully improve creative workflows, balancing quality, performance, and reliability - Design agent architectures — how agents gather context, plan, select tools, and execute creative tasks reliably at scale - Integrate state-of-the-art models, combining internal research and external capabilities to power new agent experiences - Measure and improve agent quality in production, using experimentation, evaluation frameworks, and real-world feedback to guide iteration What Makes You a Great Fit - 5+ years of professional industry experience - A track record of designing and developing ML systems or agentic pipelines in production - Deep experience with context engineering: RAG systems, token optimization, context management at scale - Experience building evaluation systems and agentic infrastructure (context gathering, tool selection, multi-step planning) - Exceptional problem solving fundamentals and ability to learn quickly in a rapidly evolving space - Able to operate effectively in an extremely fast-paced environment and scope and deliver projects end-to-end Even better if... - You have worked with multi-agent architectures - You have experience fine-tuning LLMs for specific use cases - You have a pulse on where generative media and agentic AI technology is going - You are grounded, collaborative, and willing to do whatever it takes to help the team win - You have been a startup founder or an early engineer at one 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.
Senior Machine Learning Engineer (Clinical Team)
WHAT YOU’LL DO 1. Own the tissue-class segmentation and labeling models for the ultrasound CT clinical analysis layer, and the pipelines that make them trainable and verifiable. 2. Retune across 2D per-slice, 3D volumetric, and 2D×3D fusion as reconstructed image inputs are continuously updated, and clinical indications for use expand. 3. Define training/evaluation pipelines, datasets, and metrics from the ground up or from open source; map model behavior to user needs and design requirements. 4. Work with data labeling contractors, expert clinicians, and our internal cloud/data teams on labeling specs, QC, and dataset versioning. 5. Help productionize models into a versioned, HIPAA-bound analysis service: reproducible/low-latency inference, per-prediction confidence, drift monitoring, and safe fallbacks. WHAT WE’RE LOOKING FOR - Strong applied ML experience with a track record of developing new models — architecting, training, and evaluating from scratch as well as benchmarking against existing models. - Experience with image segmentation (semantic/instance, 2D and ideally 3D/volumetric) and the modeling and training-data choices that make it robust across diverse patient anatomy. - Comfortable moving fluidly between open-ended research iteration and producing quantifiable, testable models. - Fluent in modern deep-learning tooling (e.g., PyTorch) and current development practices. - Comfortable working under design controls, where model changes carry documentation and verification weight. USEFUL EXPERIENCE - Image segmentation and label generation with modern architectures (U-Net / nnU-Net, 3D U-Net, transformer-based and promptable segmentation like SAM), including the geometry that ties voxel- and mesh-level predictions back to a coordinate frame. - Learning under limited or noisy supervision: self-supervised / semi-supervised methods (masked autoencoders, contrastive pretraining like DINO/SimCLR), active learning, weak labels, and simulation-driven pretraining. - Hands-on experience with data curation for ML: building datasets from messy, real-world sources, helping to define ground truth, and managing labeling or simulation pipelines (MONAI, ITK / SimpleITK, 3D Slicer). - Experience with segmentation models for ultrasound imaging, whether on synthetic or real images - ML for imaging or inverse problems in physics-based domains (CT, MRI, ultrasound, or adjacent), and comfort working alongside reconstruction/signal-processing teams. - Deploying models in versioned, auditable, high-stakes settings. - A background in anatomy, medical imaging, or body composition and prior work with existing segmentation models is a plus.
Research Scientist / Engineer – Training Infrastru...
You'll build the distributed systems that train Luma's large-scale multimodal models across thousands of GPUs, so researchers can focus on innovation on top of reliable, efficient, scalable infrastructure. This is hard PyTorch, CUDA, and distributed-systems work — advanced parallelism, training stability, and utilization across massive clusters. It fits an engineer who's solved real problems training foundation models at scale. If you haven't worked at the level of FSDP and multi-node training, this is the wrong depth. What You'll Own - Design, implement, and optimize efficient distributed training systems for models across thousands of GPUs. - Research and implement advanced parallelization (FSDP, Tensor Parallel, Pipeline Parallel, Expert Parallel). - Build monitoring, visualization, and debugging tools for large-scale training runs. - Optimize training stability, convergence, and resource utilization across massive clusters. First 90 Days One way the first 90 could unfold. - Days 1–30 — Immerse & Diagnose: Learn the current training stack and where stability and utilization hurt at scale. - Days 30–60 — Ship & Validate: Land a parallelization or stability improvement that measurably helps a real training run. - Days 60–90 — Scale & Systemize: Build the monitoring and tooling that keeps large runs reliable and efficient. What You Bring - Extensive distributed PyTorch training and parallelisms in foundation-model training. - Deep understanding of GPU clusters, networking, and storage systems. - Familiarity with communication libraries (NCCL, MPI) and distributed-system optimization. Nice to Have - Strong Linux systems administration and scripting. - Experience managing training runs across 100+ GPUs. - Experience with containerization, orchestration, and cloud infrastructure. About Luma: Luma's mission is to build unified general intelligence that can generate, understand, and operate in the physical world. We believe multimodality is critical for intelligence — the next step beyond language models comes from vision. Luma is an equal opportunity employer.
Research Scientist / Engineer – Performance Optimi...
You'll make Luma's multimodal models fast — profiling and optimizing GPU, CPU, and accelerator code so they train efficiently and deploy at scale without sacrificing quality. You'll write the kernels and operations that get the most out of the hardware. This is deep performance work: fused kernels, tensor cores, Triton and CUDA, distributed multi-node deployment. It fits someone with expert GPU-optimization skills and a deep understanding of transformer internals. If you're not at home in CUDA, Triton, and profilers, this is the wrong depth. What You'll Own - Profile and optimize GPU/CPU/accelerator code for maximum utilization and minimal latency. - Write high-performance PyTorch, Triton, and CUDA, dropping to custom operations when needed. - Develop fused kernels and leverage tensor cores and modern hardware features across platforms. - Optimize model architectures and implementations for distributed multi-node production deployment. - Build performance monitoring and analysis tools and automation. - Research and implement cutting-edge optimization techniques for transformer models. First 90 Days One way the first 90 could unfold. - Days 1–30 — Immerse & Diagnose: Profile the current training and inference paths and find the biggest performance wins. - Days 30–60 — Ship & Validate: Land a kernel or architecture optimization that measurably improves utilization or latency. - Days 60–90 — Scale & Systemize: Build the monitoring and automation that keeps performance gains from regressing. What You Bring - Expert-level Triton/CUDA programming and GPU optimization. - Strong PyTorch skills, including kernel development and custom operations. - Proficiency with profiling tools (NVIDIA Nsight, torch profiler, custom tooling). - Deep understanding of transformer architectures and attention mechanisms. Nice to Have - Experience with compilers and exporters (torch.compile, TensorRT, ONNX, XLA). - Experience optimizing inference workloads for latency and throughput. - Triton compiler and kernel fusion techniques. - Knowledge of warp-level intrinsics and advanced CUDA optimization. About Luma: Luma's mission is to build unified general intelligence that can generate, understand, and operate in the physical world. We believe multimodality is critical for intelligence — the next step beyond language models comes from vision. Luma is an equal opportunity employer.
Sr Principal/Principal Software Engineer, App
Your Impact at LILA Scientists shouldn't have to context-switch between a dozen tools to go from hypothesis to result. We're building the platform that makes this a reality — and we need engineers who want to solve problems no one has solved before. We're hiring Sr Principal / Principal Software Engineers to design the agents, interfaces, and platform integrations that let researchers seamlessly collaborate with AI. About The Team The Application Team sits at the center of LILA — the integration point where Machine Learning, Life Sciences, Physical Sciences, and Software become one AI-native experience that carries a scientist from hypothesis to experiment to breakthrough results. - AI isn't a feature here — it's the architecture. Agent frameworks, tools, and LLM orchestration are core primitives, not bolt-ons. - The problems are genuinely hard. Connecting AI to automated lab workflows, ML pipelines, and multi-domain knowledge graphs means inventing patterns, not copying them. - You'll learn domains you never expected. Working shoulder-to-shoulder with lab scientists and ML engineers means your technical surface area grows fast. - You'll ship things that matter. The tools you build accelerate research timelines from months to days. If you want to build at the intersection of AI and science, move fast without breaking trust, and grow into the kind of engineer who can architect systems that don't exist yet — we want to talk. What You'll Be Building - Design & Build UI and APIs: Design and build high-performance, secure, and well-documented UI and APIs that integrate with AI-driven applications. - Database Architecture & Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability. - Application Development: Drive the implementation of front-end and backend services, focusing on performance, maintainability, and reliability. - Performance & Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads. - Cloud & Infrastructure: Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale. - Cross-Functional Collaboration: Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows. What You'll Need To Succeed - Bachelor’s or Master’s degree in Computer Science, Engineering, or related field. - 8-15 years of engineering experience building and deploying large-scale systems in production. You must be strong in either front-end or backend. - Full Stack Development : Experience developing web apps across the full stack (React, TypeScript, Monorepos like Nx, TailWind, FastAPI, SQL/NoSQL, Python, Pydantic) - Hands on experience using AI coding assistants to drive productivity is required. - Communication & Collaboration : Acute listening skills, and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences. - Problem Solving : Proven ability to take ownership of complex backend challenges, balancing trade-offs between scalability, performance, and maintainability. Bonus Points For - Applied AI Engineering: Experience building with AI agents, graph-based workflows, tool-use protocols (MCP), RAG pipelines, or LLM orchestration frameworks. - Cloud & DevOps Knowledge: Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions). - Experience with ORMs: Experience with and web services for CRUD services (SQLModel, FastAPI, Django). - Orchestration Systems: Experience with orchestrators tools (Airflow, Prefect, Temporal, Dagster). - Familiarity with Python for Science : Familiarity with data science and ML libraries (pandas, numpy, scipy, jax, pytorch). - Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields. - Experience with laboratory devices, robotics, or hardware drivers. 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 $204,000 - $348,000 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 Software Engineer, App
Your Impact at LILA Scientists shouldn't have to context-switch between a dozen tools to go from hypothesis to result. We're building the platform that makes this a reality — and we need engineers who want to solve problems no one has solved before. We're hiring Sr Principal / Principal Software Engineers to design the agents, interfaces, and platform integrations that let researchers seamlessly collaborate with AI. About The Team The Application Team sits at the center of LILA — the integration point where Machine Learning, Life Sciences, Physical Sciences, and Software become one AI-native experience that carries a scientist from hypothesis to experiment to breakthrough results. - AI isn't a feature here — it's the architecture. Agent frameworks, tools, and LLM orchestration are core primitives, not bolt-ons. - The problems are genuinely hard. Connecting AI to automated lab workflows, ML pipelines, and multi-domain knowledge graphs means inventing patterns, not copying them. - You'll learn domains you never expected. Working shoulder-to-shoulder with lab scientists and ML engineers means your technical surface area grows fast. - You'll ship things that matter. The tools you build accelerate research timelines from months to days. If you want to build at the intersection of AI and science, move fast without breaking trust, and grow into the kind of engineer who can architect systems that don't exist yet — we want to talk. What You'll Be Building - Design & Build UI and APIs: Design and build high-performance, secure, and well-documented UI and APIs that integrate with AI-driven applications. - Database Architecture & Scaling: Develop schemas and manage diverse data systems (SQL, NoSQL, Vector DBs, and others) for optimal performance and scalability. - Application Development: Drive the implementation of front-end and backend services, focusing on performance, maintainability, and reliability. - Performance & Reliability: Diagnose and optimize system bottlenecks, ensuring high availability and low-latency performance across large-scale workloads. - Cloud & Infrastructure: Leverage AWS services, Kubernetes and modern DevOps practices to build and deploy production-grade systems at scale. - Cross-Functional Collaboration: Work with ML researchers, engineers, and scientists to integrate data pipelines, APIs, and cloud infrastructure into scientific workflows. What You'll Need To Succeed - Bachelor’s or Master’s degree in Computer Science, Engineering, or related field. - 5-8+ years of engineering experience building and deploying large-scale systems in production. You must be strong in backend. - Full Stack Development : Experience developing web apps across the full stack (React, TypeScript, Monorepos like Nx, TailWind, FastAPI, SQL/NoSQL, Python, Pydantic) - Hands on experience using AI coding assistants to drive productivity is required. - Communication & Collaboration : Acute listening skills, and a proven track record of working cross-functionally with scientists, data engineers, and product teams; able to explain complex ideas to diverse audiences. - Problem Solving : Proven ability to take ownership of complex backend challenges, balancing trade-offs between scalability, performance, and maintainability. Bonus Points For - Applied AI Engineering: Experience building with AI agents, graph-based workflows, tool-use protocols (MCP), RAG pipelines, or LLM orchestration frameworks. - Cloud & DevOps Knowledge: Hands-on experience with AWS; strong understanding of Kubernetes and containerization, infrastructure-as-code (Terraform, CloudFormation), and CI/CD pipelines (GitHub Actions). - Experience with ORMs: Experience with and web services for CRUD services (SQLModel, FastAPI, Django). - Orchestration Systems: Experience with orchestrators tools (Airflow, Prefect, Temporal, Dagster). - Familiarity with Python for Science : Familiarity with data science and ML libraries (pandas, numpy, scipy, jax, pytorch). - Domain Background: Exposure to laboratory software or analytics for life sciences, material sciences, or related fields. - Experience with laboratory devices, robotics, or hardware drivers. 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 $144,000 - $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.
Principal Engineer, AI Security
Your Impact at LILA Lila Sciences is building autonomous systems for scientific discovery, and the AI infrastructure behind that work needs security built in from the start. This role sits on the IT & Security team and owns security engineering for Lila's AI and ML systems, from training and inference pipelines to model registries, MCP servers, and agent runtimes. As the senior individual contributor for AI security, you will translate the AI security roadmap into delivered controls, drive vulnerability remediation to closure, and reduce risk across the full AI lifecycle: data, models, pipelines, and agentic systems. The work is hands on, with technical depth across cloud security, supply chain security, and AI-specific threat models. The role is deeply cross-functional. You will partner with AI Platform, AI Research, ML Operations, Science, and AI Safety so security shows up at design time rather than after the fact. Success looks like AI infrastructure that stays continuously compliant, measurable reduction in open risk, and engineering teams that treat security as part of how they build. What You'll Be Building - Turn the AI security roadmap into delivered controls with measurable outcomes - Design and implement security controls across training and inference pipelines, model registries, MCP servers, and agent runtimes - Keep AI infrastructure continuously compliant against internal standards and applicable frameworks - Drive vulnerability remediation across AI infrastructure, models, and dependencies to closure with system owners - Reduce risk across data, models, pipelines, and agentic systems through threat modeling, control design, and adversarial assessment - Partner with AI Platform, AI Research, ML Operations, Science, and AI Safety so security is embedded at design time What You'll Need to Succeed - Strong background in security engineering, cloud security, or DevSecOps, with hands-on experience securing production infrastructure - Working knowledge of AI/ML systems and the ML lifecycle, plus AI-specific risks such as prompt injection, model and data poisoning, insecure agent tooling, and inference-time threats - Experience with software supply chain security and dependency and vulnerability management - Experience implementing data security controls (classification, data flow mapping, access control, encryption) in cloud environments - Proven ability to partner across engineering teams and drive cross-functional remediation to closure - Comfort operating with ambiguity in a fast-moving research environment Bonus Points For - Hands-on AWS security experience - Experience with SBOM tooling at scale - Familiarity with AI governance and assurance frameworks such as NIST AI RMF - Experience with MAESTRO-style threat modeling for agentic systems 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 $252,000 - $374,000 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 Scientist I / II, Protein Design
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 builds models and systems that take a biologic from design specification to wet-lab validated lead. We're hiring a machine learning scientist to design molecules on real programs and turn what they learn into capabilities that generalize across programs. The ideal candidate is an exceptional builder with strong biological intuition: someone who can turn work on individual campaigns into reliable, extensible systems that improve how we design molecules across programs. You'll work closely with domain scientists, platform teams, and AI researchers to connect specialist protein design models to Lila's broader autonomous science platform. What You'll Be Building - Design molecules for active biologics programs, partnering with domain scientists to translate target, mechanism, and experimental constraints into actionable design hypotheses. - Develop reasoning capabilities for drug discovery, including orchestrating design workflows. - Design and maintain benchmarks and evaluation infrastructure that measure whether design workflows produce useful, generalizable decisions across biologics programs. - Own operations around reproducibility, throughput, and inference cost of computational design workflows. - Work with domain scientists to understand how designs are prioritized and turn that judgment into ML objectives and evaluation criteria. What You'll Need to Succeed - MS or PhD in computer science, machine learning, computational biology, biophysics, bioengineering, or a similar quantitative field. - Strong software engineering and system design fundamentals. - Rigor in evaluation and dataset design: how benchmarks leak, why a good validation number fails downstream, and how to measure whether an automated system is making good decisions. - Strong cross-functional communication skills. - Domain expertise in protein sequence, structure, and function. Bonus Points For - Experience building reasoning models, agents, planning systems, or multi-step ML orchestration. - Exposure to designing antibodies, nanobodies, enzymes, peptides, or other therapeutic proteins within design-test-learn loops. - Experience in developing evaluation harnesses, model registries, or benchmark suites. - Training or serving models at scale: distributed training, GPU efficiency, high-throughput inference. - 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 $176,000 - $304,000 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.
Staff Developer Advocate
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. What We're Looking For We are looking for a Staff Developer Advocate to serve as a technical champion, voice of the community, and strategic partner for the AI builders on Lighting AI. In this role, you will bridge the gap between our engineering teams and the AI builder community to drive awareness, education, and adoption through engaging technical content delivered across multiple channels. This role reports to the CMO and partners closely with Product Management, Engineering and Marketing teams. What You’ll Do - Create high-impact, educational technical content, including deep-dive tutorials, code samples, technical blogs, and reference architectures showcasing Lightning AI products. - Increase awareness of Lightning AI through engagement with the AI/ML and developer communities through keynote presentations, technical workshops, hackathons, and developer conferences. - Champion the voice of the customer to product and engineering teams. Partner closely with Product and Engineering teams to advocate for developer feedback, influencing product roadmaps and developer experience (DX). - Gaining industry recognition and credibility as a regular attendee and speaker at third party technology conferences, shows and press events. - Mentor junior developer advocates and community managers, fostering a collaborative, high-performing DevRel culture rooted in technical depth and developer empathy. - Establish experimentation and AI-native automation as core operating principles and develop testing frameworks across content form factors, channels, and topics. What You’ll Need - 10+ years of experience in Developer Relations, Developer Advocacy, Solutions Engineering, or Software Engineering within Infrastructure, developer tools, and AI/ML technologies. - Deep hands-on expertise in Python, PyTorch, Tensorflow, and modern AI/ML frameworks (e.g., LLMs, distributed training, vLLM, Hugging Face, CUDA). Solid understanding of cloud infrastructure, GPUs, distributed systems, deployment workflows for AI models, and agentic development - Exceptional public speaking and technical storytelling skills, with a proven track record of keynote speaking, podcasting, community building, and technical content creation for international audiences. - Experience developing technology solutions and evangelising end-to-end technology roadmaps that guide IT transformations toward cloud computing - Ability to work autonomously and manage multiple initiatives across different technical communities - Ability to travel (including internationally when needed). 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: $190,000 - $230,000 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.
Senior Software Engineer, Training & Experimentati...
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. What We're Looking For The Training & Experimentation team at Lightning AI builds the platform that enables developers to experiment at scale and train their own intelligence. Whether customers are training foundation models, fine-tuning open-source models, launching distributed training jobs, or iterating on experiments, this team creates the infrastructure and developer experience that powers every stage of the AI lifecycle. We're looking for a Senior Software Engineer to help design and scale the backend systems that make AI development faster, more reliable, and easier to use. You'll work across distributed training infrastructure, workload orchestration, experiment management, developer tooling, and platform APIs while partnering closely with our Managed Infrastructure, Core Platform, and Optimized Compute teams. This is an opportunity to build systems that support real-world AI workloads at scale while shaping the developer experience used by researchers, startups, and enterprise AI teams around the world. This role is based in one of our San Francisco, NYC, or London office hubs, with a minimum of 2 in-office days per week and occasional team and company offsites. We are not able to provide visa sponsorship for this position at this time. What You’ll Do - Design and build backend services that power AI agent orchestration and execution. - Develop scalable APIs and platform capabilities for tool use, workflow orchestration, memory, and state management. - Build reliable infrastructure that enables long-running, distributed AI workflows. - Partner with product, research, and platform engineering teams to bring new agent capabilities into production. - Improve platform reliability, observability, and performance as customer workloads continue to scale. - Evaluate and strengthen technical architecture, engineering processes, and developer tooling. - Maintain high standards for software quality through testing, automation, continuous delivery, and proactive management of technical debt. - Mentor engineers on system design, distributed systems, and backend engineering best practices What You’ll Need - Extensive experience building scalable backend systems using Go, Python, Rust, or similar languages. - Strong understanding of distributed systems, asynchronous processing, APIs, and cloud-native application architecture. - Experience designing and operating production services at scale. - Hands-on experience building backend platforms, developer tools, or infrastructure products. - Familiarity with cloud platforms such as AWS, GCP, or Azure and container technologies including Docker and Kubernetes. - Proven ability to take ownership of large technical initiatives from architecture through production. - Commitment to software quality, automated testing, observability, and continuous delivery. - Strong communication skills and the ability to collaborate effectively across engineering, product, research, and design teams. - Ability to thrive in fast-moving, ambiguous environments while balancing technical excellence with product impact. Compensation 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: $180,000 - $250,000 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.
Research Engineer / Scientist, Self-Improvement
SOLVING SELF-IMPROVING SUPERINTELLIGENCE The human brain is a sponge. Today’s AI brains are brittle and rigid. At Letta, we’re building self-improving artificial intelligence: creating agents that continually learn from experience and adapt over time. Founded by the creators of MemGPT from UC Berkeley’s Sky Computing Lab (the birthplace of Spark and Ray). Backed by Jeff Dean, Clem Delangue, and pioneers across AI infrastructure. Our agents already power production systems at companies like 11x https://www.letta.com/case-studies/11x and Bilt Rewards https://www.letta.com/case-studies/bilt, learning and improving every day. We’re assembling a world-class team of researchers and engineers to solve AI’s hardest problem: making machines that can reason, remember, and learn the way humans do. Note that this role is in-person (no hybrid), 5 days a week in downtown San Francisco. YOUR ROLE You will develop methods for agents that self-improve after training. At Letta, you'll work with a world-class, tight-knit team of AI researchers and engineers towards our vision of self-improving superintelligence. Advance the field through open publishing of research through papers, technical reports, blog posts, and open-source code. WHAT YOU’RE RESPONSIBLE FOR: - Developing prompt optimization and system prompt learning methods that allow agents to continuously learn from long-horizon tasks - Developing methods allow agents automatically generate and test out hypotheses in an environment, or seek out and improve their own weaknesses - Improving the efficiency of learning from long-running agentic tasks - Designing and running experiments to measure self-improvement scalably WHAT WE'RE LOOKING FOR: - Expertise in machine learning, in particular LLMs and continual learning - Familiarity with agent frameworks and prompt optimization - Track record of impactful research (breakthrough publications and/or open-source contributions) - Ability to balance execution speed with empirical rigor - Real-world impact beyond pure academic work OUR CULTURE SIGNS IT COULD BE A GREAT FIT: - You want to maximize your impact: you want work on a small, incredibly talented team where every individual plays a huge role in the team's success. You wonder what it would have been like to be at OpenAI when it was just a dozen people, or Google when it was just a couple of grad students in a garage. - You’re excited to go head-to-head with tech giants, frontier labs, and other startups that are many times our size in both headcount and funding. - You are fundamentally opposed to closed frontier AI that is controlled by a handful of billionaires and private tech companies. SIGNS IT’S A BAD FIT: - You like stability, and get stressed out when there’s nobody telling you exactly what to do. We look for people that thrive in ambiguity and can intuit the most important problems by talking to customers and dogfooding our product. - You want to work a 9-5, and value clear separation of work from life. The stakes are the highest they've ever been, and the only moat is execution velocity. Operating on a strict 9-5 guarantees failure. - You value titles or want to people-manage. Letta is a flat company where every researcher and engineer is an individual contributor. OUR INTERVIEW PROCESS: - Initial screen (30 min) - Technical screen (1-1.5 hours) - Paid in-person work trial (2 days onsite in SF)
Machine Learning Scientist
THE OPPORTUNITY We are looking for a highly skilled machine learning researcher with significant experience in generative modeling. 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 architect novel generative models with the goal of designing new proteins that are functional in wet lab assays. WHO YOU ARE - You are a strong ML researcher with experience in generative modeling. You have led or 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 proven track record of deep expertise in generative modeling. - You are a skillful 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 running training and inference on cloud hardware, parallelizing data and models across accelerators. - You are a data engineer. You have experience building ML data pipelines for the training and evaluation of deep learning models. You are able to analyze the raw data, construct appropriate dataset splits and build pipelines that perform and scale. - You are passionate about model performance. You have an intricate understanding of how ML libraries interplay with hardware and data and love to optimize deep learning models for training / inference speed. You have a deep knowledge of best principles and tricks in architecting deep learning models and use it to optimize how they perform on validation metrics. - 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. You work well in a fast-paced setting where goals must be achieved efficiently and urgently. WHAT SETS YOU APART - You have experience in computational biology or protein design. You have worked on ML-driven projects in biology. - You have a natural science background. You are academically trained in physics, biology, chemistry or other related fields. YOUR RESPONSIBILITIES - Build machine learning models that work in the physical world [~90% of your time]: - Contribute to a careful curation of our training and evaluation data. - Propose and build ML evaluation metrics that align with real world success and company goals. - Quickly prototype generative models against our lead metrics and perform deep analyses of improvements. - Collaborate in a joint codebase with other research scientists, engineers and protein designers, maintaining highest code standards. - Contribute to the maintenance of our compute and ML development infrastructure. - In collaboration with the bio team, plan wet lab testing campaigns and carry out model inference against biological targets to enable their testing in the wet lab. - Quickly learn from wet lab results and feedback data to our models. - Self development [~10% of your time]: - Stay on top of the latest developments in ML. - Gain a strong working understanding of protein and cell biology. - Participate in knowledge sharing, e.g. organize 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/401(K) 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 Solution Engineer
Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers. Our customers range from AI researchers to enterprises and hyperscalers. Lambda's mission is to make compute as ubiquitous as electricity and give everyone the power of superintelligence. One person, one GPU. If you'd like to build the world's best AI cloud, join us. *Note: This position requires presence in our San Francisco, San Jose, or Bellevue office location 4 days per week; Lambda’s designated work from home day is currently Tuesday. The Lambda Cloud GTM team powers our growth by enabling customers to realize their business goals with AI infrastructure. We partner with leading AI researchers and enterprise engineering teams to design, scale, and optimize high-performance GPU cloud solutions. Driven by technical mastery, agility, and a customer-first mindset, our team turns massive compute challenges into seamless, production-ready AI infrastructure. What You’ll Do - Drive technical sales & executive influence - Partner with Account Executives to lead complex deals with large enterprises and digital native businesses and build trusted relationships with technical leaders (CTOs, Heads of AI/ML, Platform Leads) - Evaluate customer architectural needs, uncover potential bottlenecks, and design end-to-end GPU cloud solutions - Author comprehensive proposals & architecture diagrams and collaborate with teams on Bill of Materials (BOMs), and rack elevations for multi-node GPU clusters - Lead hands-on proof-of-concept (PoC) activities & benchmarking for customers - Design, execute, and deliver technical PoCs and custom prototypes to demonstrate Lambda’s performance, reliability, and value - Run benchmark evaluations across training and inference workloads to show tangible performance and cost advantages over competitors - Architect & optimize AI/ML workloads - Guide enterprise engineering teams on structuring their AI lifecycle—from data ingestion and distributed training (SLURM, Kubernetes) to inference optimization (vLLM, TensorRT-LLM) and observability - Provide architectural guidance on high-performance networking (InfiniBand, RoCE), distributed storage, and cluster topologies to ensure maximum GPU utilization - Champion customer feedback & product advocacy - Serve as the technical voice of the customer internally, funneling field insights, product gaps, and feature requests directly to Lambda’s Product and Engineering teams - Create field enablement assets, technical whitepapers, architectural blueprints, and lead technical workshops for prospective clients & partners - Represent Lambda as a subject matter expert at industry conferences, webinars, and technical community events - Reinforce Lambda’s culture - Contribute positively throughout the organization - Maintain a high level of agility and responsiveness - Hyper-focused on customer satisfaction You - Have a proven track record deploying, benchmarking, and optimizing workloads on NVIDIA GPU architectures (e.g., HGX platforms, NVLink) using deep learning frameworks (PyTorch, NeMo) and inference engines (vLLM, TensorRT-LLM) - Have 8+ years of experience designing, deploying, and scaling enterprise cloud infrastructure - Have 4+ years in a Solution Architect, Solution Engineer, or technical customer-facing capacity supporting complex cloud environments - Have 3+ years of hands-on experience architecting and deploying cloud-based AI/ML workloads - Have strong experience with modern infrastructure orchestration tools such as Kubernetes, Docker, SLURM, Terraform, and Ansible - Have deep knowledge of cloud networking concepts, including high-speed interconnects (InfiniBand, RoCE), distributed file systems (NFS, NVMe-oF, Weka, VAST), security, and cost optimization - Have experience coding in Python, Go, C/C++ (CUDA) or similar programming language - Have experience partnering with Account Executives to close complex cloud deals, present technical architectures to C-level stakeholders (CTOs, VP of Eng), and drive customer alignment - Have demonstrated impact at an organizational/multi-departmental level and are effective mentoring junior SEs or architects - Thrive in dynamic settings and embrace radical ownership of initiatives and outcomes Nice to Have - Direct experience with end-to-end LLM fine-tuning, algorithm selection, pipeline design, or distributed training setups (3D parallelism, Megatron-LM) - Prior experience with product launches, leading GTM initiatives, or publishing technical whitepapers/benchmarks - Experience integrating RESTful APIs, gRPC, and service-oriented cloud architectures Salary Range Information The annual salary range for this position has been set based on market data and other factors. However, a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description. About Lambda - Founded in 2012, with 500+ employees, and growing fast - Our investors notably include TWG Global, US Innovative Technology Fund (USIT), Andra Capital, SGW, Andrej Karpathy, ARK Invest, Fincadia Advisors, G Squared, In-Q-Tel (IQT), KHK & Partners, NVIDIA, Pegatron, Supermicro, Wistron, Wiwynn, Gradient Ventures, Mercato Partners, SVB, 1517, and Crescent Cove - We have research papers accepted at top machine learning and graphics conferences, including NeurIPS, ICCV, SIGGRAPH, and TOG - Our values are publicly available: https://lambda.ai/careers - We offer generous cash & equity compensation - Health, dental, and vision coverage for you and your dependents - Wellness and commuter stipends for select roles - 401k Plan with 2% company match (USA employees) - Flexible paid time off plan that we all actually use Equal Opportunity Employer Lambda is an Equal Opportunity employer. Applicants are considered without regard to race, color, religion, creed, national origin, age, sex, gender, marital status, sexual orientation and identity, genetic information, veteran status, citizenship, or any other factors prohibited by local, state, or federal law.
Member of Technical Staff
Shape the Future of AI At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially. About Labelbox We're the only company offering three integrated solutions for frontier AI development: - Enterprise Platform & Tools : Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale - Frontier Data Labeling Service : Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models - Expert Marketplace : Connecting AI teams with highly skilled annotators and domain experts for flexible scaling Why Join Us - High-Impact Environment : We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions. - Technical Excellence : Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence. - Innovation at Speed : We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution. - Continuous Growth : Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI. - Clear Ownership : You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics. The Role We’re hiring a Member of Technical Staff to own the design, development, and production of Frontier Data Products. You’ll build the sandboxed, reproducible environments AI agents rely on during training and evaluation, the terminals, browsers, and tool-augmented workspaces they operate inside. This is a hands-on engineering role. You’ll write production-quality infrastructure, integrate with the broader RL tooling ecosystem, and partner closely with our data operations team to keep environments robust and observable for annotators and model agents alike. Above all, you’ll need a real grasp of how RL training loops consume environments and where they tend to break. What You’ll Do - Design, build, and maintain sandboxed RL environments for agentic AI training—including terminal emulators, browser automation harnesses, computer-use simulators, and tool-augmented workspaces (e.g., environments built on frameworks like TerminalBench, OSWorld, and Tau-bench) - Develop reproducible, containerized execution environments (Docker, VMs, lightweight sandboxes) that support deterministic task rollouts and reward signal collection - Integrate with and extend open-source agentic tooling and custom CLI/API harnesses to enable multi-step agent interaction - Build instrumentation and observability layers- structured logging, trajectory capture, state snapshotting, so training runs and human annotation sessions produce clean, auditable data - Collaborate with data operations to design task curricula and evaluation protocols that stress-test model capabilities across environment types - Own environment deployment and reliability: CI/CD pipelines, automated testing of environment configurations, and monitoring for drift or breakage across versions - Rapidly prototype new environment types as client and internal requirements evolve, moving from spec to working system in days, not weeks What We’re Looking For - 2+ years of professional software engineering experience, with strong fundamentals in Python and at least one systems-level language (Go, Rust, C++) - Demonstrated experience with containerization and sandboxing (Docker, Podman, Firecracker, or similar) in production or near-production contexts - Familiarity with RL concepts: MDPs, reward shaping, episode structure, observation/action spaces. You don’t need to have trained models, but you need to understand what an environment must provide to an RL training loop - Experience building or maintaining developer tooling, CLI tools, or infrastructure automation - Comfort working with browser automation frameworks or terminal interaction tooling - Strong debugging instincts, you can trace failures across process boundaries, container layers, and network calls - Ability to read and implement from academic papers and open-source benchmark repositories without extensive hand-holding Preferred - Direct experience building or contributing to RL environments (Gymnasium/Gym, PettingZoo, or custom environment implementations) - Experience with agentic AI evaluation frameworks (SWE-bench, WebArena, OSWorld, TerminalBench, or similar) - Familiarity with GCP or AWS infrastructure (Compute Engine, ECS/EKS, Cloud Build) - Prior work at an AI data company, ML platform company, or AI research lab - Contributions to open-source projects in the RL, agents, or dev-tools space Candidate Archetype The ideal candidate is a strong software engineer first, with genuine curiosity and working knowledge of how modern AI systems are built and evaluated. You’ve probably built infrastructure or developer tooling at a startup or mid-stage company, and you’ve been pulled toward the AI space maybe through side projects, open-source contributions, or a prior role adjacent to an applied AI or ML team. You’re the kind of engineer who reads a benchmark paper or evaluation framework and immediately thinks about how to make the underlying system more robust, not just how to improve the model’s output. You thrive in ambiguity. You can take a loosely defined project requirement, “build an environment that tests an agent’s ability to navigate a file system and execute multi-step bash workflows” and deliver a working, tested, documented system without needing a detailed spec. You move fast, but you care about reliability because you know systems that break silently poison the data everything downstream depends on. Why This Role Matters - Reinforcement learning has become the state-of-the-art approach for training agentic AI, and environment quality is one of its biggest bottlenecks. Environments that are brittle, non-deterministic, or poorly instrumented produce noisy signals that directly degrade model performance. You’ll be solving one of the highest-leverage infrastructure problems in AI today. - You’ll work across a portfolio of projects spanning different AI labs and model capabilities with no single-product monotony. The environment types you build will evolve as RL continues to shape the frontier of agent capabilities. - Alignerr is a small, high-impact team inside Labelbox. You’ll have startup-level ownership with growth-stage resources. Labelbox strives to ensure pay parity across the organization and discuss compensation transparently. The expected annual base salary range for United States-based candidates is below. This range is not inclusive of any potential equity packages or additional benefits. Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location. Annual base salary range $140,000 - $200,000 USD Life at Labelbox - Environment : Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making - Growth : Career advancement opportunities directly tied to your impact - Vision : Be part of building the foundation for humanity's most transformative technology Our Vision We believe data will remain crucial in achieving artificial general intelligence. As AI models become more sophisticated, the need for high-quality, specialized training data will only grow. Join us in developing new products and services that enable the next generation of AI breakthroughs. Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins. Our customers include Fortune 500 enterprises and leading AI labs. Your Personal Data Privacy : Any personal information you provide Labelbox as a part of your application will be processed in accordance with Labelbox’s Job Applicant Privacy notice . Any emails from Labelbox team members will originate from a @labelbox.com email address. If you encounter anything that raises suspicions during your interactions, we encourage you to exercise caution and suspend or discontinue communications.
Forward Deployed Engineer, RL Environments
Shape the Future of AI At Labelbox, we're building the critical infrastructure that powers breakthrough AI models at leading research labs and enterprises. Since 2018, we've been pioneering data-centric approaches that are fundamental to AI development, and our work becomes even more essential as AI capabilities expand exponentially. About Labelbox We're the only company offering three integrated solutions for frontier AI development: - Enterprise Platform & Tools : Advanced annotation tools, workflow automation, and quality control systems that enable teams to produce high-quality training data at scale - Frontier Data Labeling Service : Specialized data labeling through Alignerr, leveraging subject matter experts for next-generation AI models - Expert Marketplace : Connecting AI teams with highly skilled annotators and domain experts for flexible scaling Why Join Us - High-Impact Environment : We operate like an early-stage startup, focusing on impact over process. You'll take on expanded responsibilities quickly, with career growth directly tied to your contributions. - Technical Excellence : Work at the cutting edge of AI development, collaborating with industry leaders and shaping the future of artificial intelligence. - Innovation at Speed : We celebrate those who take ownership, move fast, and deliver impact. Our environment rewards high agency and rapid execution. - Continuous Growth : Every role requires continuous learning and evolution. You'll be surrounded by curious minds solving complex problems at the frontier of AI. - Clear Ownership : You'll know exactly what you're responsible for and have the autonomy to execute. We empower people to drive results through clear ownership and metrics. The Role We’re hiring a Forward Deployed Engineer to own the design, development, and operationalization of reinforcement learning environments. You’ll build the sandboxed, reproducible execution environments that AI agents interact with during training and evaluation—things like terminal-based task benchmarks, browser and computer-use environments, and tool-augmented agentic workspaces. This is a hands-on engineering role. You’ll write production-quality infrastructure code, integrate with open-source RL tooling, and work closely with our data operations team to ensure environments are robust, observable, and ready for human annotators and model agents alike. You won’t be doing ML research, but you’ll need to deeply understand how RL training loops consume environments and where the bottlenecks live. What You’ll Do - Design, build, and maintain sandboxed RL environments for agentic AI training—including terminal emulators, browser automation harnesses, computer-use simulators, and tool-augmented workspaces (e.g., environments built on frameworks like TerminalBench, OSWorld, and Tau-bench) - Develop reproducible, containerized execution environments (Docker, VMs, lightweight sandboxes) that support deterministic task rollouts and reward signal collection - Integrate with and extend open-source agentic tooling and custom CLI/API harnesses to enable multi-step agent interaction - Build instrumentation and observability layers—structured logging, trajectory capture, state snapshotting—so training runs and human annotation sessions produce clean, auditable data - Collaborate with data operations to design task curricula and evaluation protocols that stress-test model capabilities across environment types - Own environment deployment and reliability: CI/CD pipelines, automated testing of environment configurations, and monitoring for drift or breakage across versions - Rapidly prototype new environment types as client and internal requirements evolve, moving from spec to working system in days, not weeks What We’re Looking For Required - 2+ years of professional software engineering experience, with strong fundamentals in Python and at least one systems-level language (Go, Rust, C++) - Demonstrated experience with containerization and sandboxing (Docker, Podman, Firecracker, or similar) in production or near-production contexts - Familiarity with RL concepts: MDPs, reward shaping, episode structure, observation/action spaces. You don’t need to have trained models, but you need to understand what an environment must provide to an RL training loop - Experience building or maintaining developer tooling, CLI tools, or infrastructure automation - Comfort working with browser automation frameworks or terminal interaction tooling - Strong debugging instincts—you can trace failures across process boundaries, container layers, and network calls - Ability to read and implement from academic papers and open-source benchmark repositories without extensive hand-holding Preferred - Direct experience building or contributing to RL environments (Gymnasium/Gym, PettingZoo, or custom environment implementations) - Experience with agentic AI evaluation frameworks (SWE-bench, WebArena, OSWorld, TerminalBench, or similar) - Familiarity with GCP or AWS infrastructure (Compute Engine, ECS/EKS, Cloud Build) - Prior work at an AI data company, ML platform company, or AI research lab - Contributions to open-source projects in the RL, agents, or dev-tools space Candidate Archetype The ideal candidate is a strong software engineer first, with genuine curiosity and working knowledge of reinforcement learning. You’ve probably built infrastructure or developer tooling at a startup or mid-stage company, and you’ve been pulled toward the ML/AI space—maybe through side projects, open-source contributions, or a prior role adjacent to an ML team. You’re the kind of engineer who reads an RL benchmark paper and immediately thinks about how to make the environment more robust, not how to improve the policy gradient. You thrive in ambiguity. You can take a loosely defined project requirement—“build an environment that tests an agent’s ability to navigate a file system and execute multi-step bash workflows”—and deliver a working, tested, documented system without needing a detailed spec. You move fast, but you care about reliability because you know environments that break silently poison training data. Why This Role Matters - RL environment quality is one of the biggest bottlenecks in agentic AI training today. Environments that are brittle, non-deterministic, or poorly instrumented produce noisy reward signals that directly degrade model performance. You’ll be solving one of the highest-leverage infrastructure problems in AI. - You’ll work across a portfolio of projects spanning different AI labs and model capabilities—no single-product monotony. The environment types you build will evolve as the frontier of agent capabilities moves. - Alignerr is a small, high-impact team inside a well-funded company (Labelbox). You’ll have startup-level ownership with growth-stage resources. Alignerr Services at Labelbox Alignerr is Labelbox’s human data organization, purpose-built to generate the high-quality training data that powers the next generation of AI models. We partner directly with leading AI labs to produce reinforcement learning environments, evaluation benchmarks, and expert-annotated datasets that push model capabilities forward. Our team sits at the intersection of software engineering, ML infrastructure, and human-in-the-loop data production. Labelbox strives to ensure pay parity across the organization and discuss compensation transparently. The expected annual base salary range for United States-based candidates is below. This range is not inclusive of any potential equity packages or additional benefits. Exact compensation varies based on a variety of factors, including skills and competencies, experience, and geographical location. Annual base salary range $140,000 - $200,000 USD Life at Labelbox - Environment : Fast-paced and high-intensity, perfect for ambitious individuals who thrive on ownership and quick decision-making - Growth : Career advancement opportunities directly tied to your impact - Vision : Be part of building the foundation for humanity's most transformative technology Our Vision We believe data will remain crucial in achieving artificial general intelligence. As AI models become more sophisticated, the need for high-quality, specialized training data will only grow. Join us in developing new products and services that enable the next generation of AI breakthroughs. Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins. Our customers include Fortune 500 enterprises and leading AI labs. Your Personal Data Privacy : Any personal information you provide Labelbox as a part of your application will be processed in accordance with Labelbox’s Job Applicant Privacy notice . Any emails from Labelbox team members will originate from a @labelbox.com email address. If you encounter anything that raises suspicions during your interactions, we encourage you to exercise caution and suspend or discontinue communications.
Machine Learning Engineer
ABOUT KREA At Krea, we are building next-generation AI creative tools. We're dedicated to making AI intuitive and controllable for creatives - our mission is to build tools that empower human creativity, not replace it. We believe AI is a new medium that allows us to express ourselves through various formats - text, images, video, sound, and even 3D. We're building better, smarter, and more controllable tools to harness this medium. We recently took this a step forward with the launch of Krea 2 https://www.krea.ai/blog/krea-2-image-model, our first foundation model, built completely from scratch for aesthetic diversity and stylistic control. We've raised over $83M and are backed by world-class investors such as a16z, Bain Capital, and Abstract. We work full-time and in-person at our waterfront office in San Francisco. We care about creativity: our team includes musicians, designers, visual artists, and engineers. THIS ROLE We're looking for a machine learning engineers who can work on large-scale image and video models training experiments. WHAT YOU'LL DO - Train foundation diffusion models for image and video generation - Train controllability modules such as IPAdapters or ControlNets - Develop novel research techniques and put them into production - Conducting large-scale experiments on high-performance computing clusters, optimizing data pipelines for massive image datasets WHAT WE'RE LOOKING FOR - Proven track record in working with image or video models at scale (publications or open-source contributions a plus) - Strong background in deep learning frameworks and distributed training paradigms - Ability to iterate rapidly, and propose creative research directions WHAT WE OFFER - Team: Work alongside a world-class team building the future of AI creative tooling - Impact: Significant scope and company-wide impact - Competitive compensation: generous salary & equity packages - Health & wellness: 100% health & 99% dental/vision insurance premiums covered for employees, health FSA accounts, & long-term disability coverage - Time off: Flexible PTO policy - Financial planning: 401k with a 4% company-sponsored match - Meals in the office: breakfast, lunch, dinner - you name it, we'll cover it - Transit: Ubers covered to & from the office - Sponsorship: We're open to sponsoring international visas where we can (e.g., STEM OPT, OPT, H-1B, O-1, E-3). - And more! Please note the above benefits & perks are for full-time employees
Software Engineer (ML Infrastructure), London
Isomorphic Labs is applying frontier AI to help unlock deeper scientific insights, faster breakthroughs, and life-changing medicines with an ambition to solve all disease. The future is coming. A future enabled and enriched by the incredible power of machine learning. A future in which diseases are curtailed or cured starting with better and faster drug discovery. Come and be part of an interdisciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards us achieving our ambitious goals, while being a part of an inspiring and collaborative culture. The world we want tomorrow is the one we’re building today. It starts with the culture at this company. It starts with you. About Iso Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed. Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can use it to model complex biological phenomena to help design novel molecules, anticipate how drugs will perform and develop innovative medicines to treat and cure some of the world’s most devastating diseases. We have built a world-leading drug design engine comprising AI models that are capable of working across multiple therapeutic areas and drug modalities. We are continually innovating on model architecture and developing cutting-edge capabilities to advance rational drug design. Every day, and with each new breakthrough, we’re getting closer to the promise of digital biology, and achieving our ambitious mission to one day solve all disease with the help of AI. Your impact This is an exciting opportunity for a small number of innovative and versatile software engineers to work in a fast-paced and research-centric environment. You will collaborate extensively with internal engineering and research teams to evolve the foundational infrastructure that makes it easy to train and deploy novel machine learning models over vast (and growing) pools of compute and data. For this role we are looking for individuals with experience of designing and evolving complex systems at significant scale. Domain knowledge of ML inference, training or research infrastructure will be a strong advantage, though we encourage applications from candidates with a record of exceptional impact in any field. What you will do - Evolve the ML infrastructure platform to accelerate both human and agent-driven research at scale - Explore options to deploy agents to increase the robustness and reliability of the platform - Develop the platform to enable the next generation of distributed, performance-critical training and inference workloads - Partner closely with engineering, research and product teams to identify and deliver impact Skills and qualifications Essential: - Demonstrated ability to independently identify opportunities, deliver solutions and work effectively in cross-functional teams - Experience building and evolving infrastructure or platforms at significant scale - Strong Python programming (though familiarity with any particular library is not required) Nice to have: - Domain knowledge of ML inference, training or research infrastructure - Strong hands-on experience building for Kubernetes - Experience providing platforms to internal customers - Experience with Google Cloud Platform (GCP) - Experience with multi-cloud environments Culture and values We are guided by our shared values. It's not about finding people who think and act in the same way. These values help to guide our work and will continue to strengthen it. Thoughtful Thoughtful at Iso is about curiosity, creativity and care. It is about good people doing good, rigorous and future-making science every single day. Brave Brave at Iso is about fearlessness, but it’s also about initiative and integrity. The scale of the challenge demands nothing less. Determined Determined at Iso is the way we pursue our goal. It’s a confidence in our hypothesis, as well as the urgency and agility needed to deliver on it. Because disease won’t wait, so neither should we. Together Together at Iso is about connection, collaboration across fields and catalytic relationships. It’s knowing that transformation is a group project, and remembering that what we’re doing will have a real impact on real people everywhere. Creating an extraordinary company We believe that to be successful we need a team with a range of skills and talents. We're building an environment where collaboration is fundamental, learning is shared and every employee feels supported and able to thrive. We value unique experiences, knowledge, backgrounds, and perspectives, and harness these qualities to create extraordinary impact. We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know. Hybrid working It’s hugely important for us to share knowledge and build strong relationships with each other, and we find it easier to do this if we spend time together in person. This is why we follow a hybrid model, and would require you to be able to come into the office 3 days a week (currently Tuesday, Wednesday, and one other day depending on which team you’re in). If you have additional needs that would prevent you from following this hybrid approach, we’d be happy to talk through these if you’re selected for an initial screening call. Please note that when you submit an application, your data will be processed in line with our privacy policy . >> Click to view other open roles at Isomorphic Labs
Senior Machine Learning Scientist, Imaging
THE OPPORTUNITY Imaging based phenotyping of in-vitro biology is at the heart of insitro's efforts to accelerate drug development. At insitro, we use machine learning to derive clinically relevant insights from rich datasets generated in-house. As an Imaging ML Scientist, you will develop ML-empowered computer vision pipelines to extract insights about disease mechanisms from multiple microscopy modalities. You will be part of a cross-functional team of life scientists, software engineers, computational biologists, and machine learning scientists that strive to identify therapeutic targets and develop drugs of high efficacy and low toxicity. You will be joining a vibrant biotech startup that is in a high growth phase, with promising multiple pre-clinical drug targets in areas such as ALS and metabolic disease. A lot can change in this exciting phase, providing many opportunities for significant impact. You will work closely with a very talented team, learn a broad range of skills, and help shape insitro's culture, strategic direction, and outcomes. This role will be reporting to the Director of Imaging, Cellular Machine Learning. This is a hybrid position that requires you to be in our South San Francisco headquarters at least three days per week. Join us, and help make a difference to patients! RESPONSIBILITIES - Partner with experimental and computational biologists to design, troubleshoot, and optimize high-throughput imaging-based experiments and workflows - Identify, understand, develop, and deploy novel computer vision and machine learning methods such as segmentation, feature extraction, and representation learning to extract features from microscopy image datasets - Work closely with software engineers to build robust and well-tested image analysis workflows, able to be used by experimental and computational biologists with minimal direct support - Calibrate analysis tools and workflows, define performance metrics, and conduct benchmarking to select fit-for-purpose solutions - Communicate findings to cross-functional stakeholders through reports, visualizations, presentations, and publications - Identify novel disease-relevant phenotypes and propose new screening paradigms that translate to actionable program decisions ABOUT YOU - Ph.D. in computer vision, machine learning, computer science or a related discipline, or equivalent practical experience (e.g., a Master's degree and 2+ years of relevant industry experience) - Ability to communicate effectively and collaborate with people of diverse backgrounds and job functions on a daily basis - Demonstrated ability to identify, understand, and find novel applications of state-of-the-art ML/CV methods from literature or other sources - Strong programming skills in Python, including working with TB-scale image datasets and leveraging modern agentic software tools to accelerate development - Experience developing models for diverse computer vision tasks (e.g. segmentation, recognition, classification, domain adaptation) including using modern deep learning frameworks (PyTorch, TensorFlow, Keras, etc) - Extensive hands on experience working with microscopy data or similar biomedical or physical imaging modalities - Passion for making a difference in the world NICE TO HAVE - Demonstrated ability to write software in a team, industry experience or substantial involvement with open source projects. - Experience modeling with video or imaging time-series datasets - Experience with microscopy hardware, and/or image data acquisition in other contexts - Understanding of optical physics, resolution, numerical aperture, etc. - Familiarity with cloud computing services (e.g., AWS or Azure) - Experience with database languages (e.g., SQL) - Publication record of meaningful contributions to high-quality work in relevant computer vision, clinical ML, or biomedical venues COMPENSATION & BENEFITS AT INSITRO Our target starting salary for successful US-based applicants for this role is $215,000 - $235,000. To determine starting pay, we consider multiple job-related factors including a candidate's skills, education and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data. This role is eligible for participation in our Annual Performance Bonus Plan (based on company targets by role level and annual company performance) and our Equity Incentive Plan, subject to the terms of those plans and associated policies. In addition, insitro also provides our employees: - 401(k) plan with employer matching for contributions - Excellent medical, dental, and vision coverage as well as mental health and well-being support - Open, flexible vacation policy - Paid parental leave of at least 16 weeks to support parents who give birth, and 10 weeks for a new parent (inclusive of birth, adoption, fostering, etc) - Quarterly budget for books and online courses for self-development - Support to attend professional conferences that are meaningful to your career growth and role's responsibilities - New hire stipend for home office setup - Monthly cell phone & internet stipend - Access to free onsite baristas and daily lunch for employees who are either onsite or hybrid - Access to a free commuter bus network that provides transport to and from our South San Francisco HQ from locations all around the Bay Area insitro is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. We believe diversity, equity, and inclusion need to be at the foundation of our culture. We work hard to bring together diverse teams–grounded in a wide range of expertise and life experiences–and work even harder to ensure those teams thrive in inclusive, growth-oriented environments supported by equitable company and team practices. All candidates can expect equitable treatment, respect, and fairness throughout the interview process. Please be aware of recruitment scams: we never request payments, all recruitment communications are from @insitro.com http://insitro.com, and if in doubt, contact us at info@insitro.com. #LI-Hybrid About insitro insitro is a drug discovery and development company using machine learning (ML) and data at scale to decode biology for transformative medicines. At the core of insitro’s approach is the convergence of in-house generated multi-modal cellular data and high-content phenotypic human cohort data. We rely on these data to develop ML-driven, predictive disease models that uncover underlying biologic state and elucidate critical drivers of disease. These powerful models rely on extensive biological and computational infrastructure and allow insitro to advance novel targets and patient biomarkers, design therapeutics and inform clinical strategy. insitro is advancing a wholly owned and partnered pipeline of insights and therapeutics in neuroscience and metabolism. Since launching in 2018, insitro has raised over $700 million from top tech, biotech and crossover investors, and from collaborations with pharmaceutical partners. For more information on insitro, please visit www.insitro.com http://www.insitro.com.
Data selection and quality evaluation for biologic...
At Inceptive, you will help pioneer the next generation of AI-designed drugs, with the potential to positively impact billions of people, as part of a collaborative, antedisciplinary team. We advance the state of the art in molecular design by training large-scale foundation models that enable cutting-edge generative approaches. Those models depend on rich, high-quality experimental data that captures biological function. Progress requires not only building better models, but also designing better experiments, understanding measurement systems, and generating datasets that faithfully represent underlying biology. You will collaborate closely with biologists and machine learning researchers to design, analyze, and improve the experiments that power our models. You will help determine what data should be generated, how experiments should be structured, how measurement artifacts can be identified, and how biological insights can be translated into scalable data generation strategies. Your Mission, should you choose to accept it - Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise - Develop statistical and computational approaches to characterize assay quality, reproducibility, and sources of experimental variation - Identify and investigate sources of bias and measurement artifacts in biological datasets - Design and analyze large-scale biological experiments that generate training and evaluation data for machine learning models - Partner with experimental scientists to improve assay design, controls, and data collection strategies - Collaborate with machine learning researchers to understand how experimental design decisions impact model training and evaluation - Analyze, visualize, and communicate findings to support decision-making across scientific and engineering teams Qualifications - PhD in computational biology, systems biology, genomics, bioengineering, biostatistics, biophysics, or a related quantitative discipline, or equivalent practical experience - Demonstrated track record of analyzing complex biological datasets and translating computational insights into experimental validation or new data collection - Strong foundation in experimental design, statistical analysis, and quantitative reasoning - Deep understanding of sources of experimental variability, batch effects, and assay artifacts in biological data - Capable programmer in Python and common scientific computing libraries - Excellent written and verbal communication skills, including the ability to communicate effectively across computational and experimental disciplines - Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET - Readiness to travel several times a year for company retreats and business events - We value the benefits of in-person collaboration and expect candidates to primarily work from our office locations Preferred technical skills - 3+ years of post-PhD experience in computational biology, biostatistics, or a related field - Experience connecting experimental outcomes to machine learning model development and evaluation Compensation $135K – $240K + Bonus + Equity What we offer - A competitive compensation package - 30 days paid vacation per year - Comprehensive health insurance for US based Beginners - 401K with company match for US based Beginners and Direktversicherung for German Beginners - Quarterly company-wide retreats - Monthly wellness benefit - Budget for multiple visits per year to our offices in Berlin, Palo Alto or Switzerland - Learning & Development budget to attend conferences, take courses, or otherwise invest in your professional growth - A buddy to help you get settled *Varies by country and does not apply to internships At Inceptive, we are creating tools to develop increasingly powerful biological software for the rational design of novel, broadly accessible medicines and biotechnologies previously out of reach. Our team brings together vast expertise in molecular biology, machine learning, and software engineering, and we are all working towards becoming antedisciplinary, meaning we deepen the knowledge we have in our area of expertise while also expanding our knowledge of completely new fields. We approach our goals with a Beginner's mind, humbly and with fresh eyes, and aim to become the pioneers of a new discipline rooted in biology as much as in deep learning, whose impact will be realized together with out-of-the-box thinkers in business and entrepreneurship, defying established categorizations. We are building a company culture centered around growth, learning, and discovery. We believe in humility and open-mindedness in how we approach each other, as well as problems we don't yet have solutions for. It is the policy of Inceptive to ensure equal employment opportunity without discrimination or harassment on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, age, disability, marital status, citizenship, national origin, genetic information, or any other characteristic protected by law. Inceptive prohibits any such discrimination or harassment. Inceptive is also committed to welcoming and providing accommodations to people with disabilities. Please let us know if you need any accommodations throughout your application process.
Computational design of biological experiments for...
At Inceptive, you will help pioneer the next generation of AI-designed drugs, with the potential to positively impact billions of people, as part of a collaborative, antedisciplinary team. We advance the state of the art in molecular design by training large-scale foundation models that enable cutting-edge generative approaches. Those models learn from diverse biological datasets and are refined through focused experimentation, large-scale training, and feedback from lab measurements. Progress depends not only on building better models, but also on understanding what they learn, where they fail, how data shapes their behavior, and how to evaluate them against biologically meaningful objectives. You will collaborate closely with AI researchers and biologists to rigorously characterize the behavior, capabilities, and limitations of biological foundation models and their applications. You will identify valuable datasets, develop meaningful evaluations, investigate model behavior, and generate insights that guide model development, data strategy, and experimental priorities across the company. Your Mission, should you choose to accept it - Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise - Investigate how model performance changes with data quantity, data quality, dataset composition, and training methodology - Develop biologically meaningful evaluations and benchmarks that measure progress toward therapeutic design objectives - Design and execute rigorous experiments to understand the behavior, capabilities, and limitations of biological foundation models - Identify sources of potential artifacts, bias, and noise in biological datasets - Identify promising biological datasets for model training and evaluation, and develop computational pipelines for preprocessing, quality control, and exploratory analysis. - Design studies, in silico or in the lab, that reveal what models have learned and which biological signals drive model behavior - Work with biologists to formulate hypotheses and translate biological questions into measurable machine learning experiments - Partner with AI researchers and engineers to prioritize research directions, data collection efforts, and model improvements - Analyze, visualize, and communicate experimental findings to inform decisions across teams Qualifications - PhD in computational biology, statistics, physics, machine learning, or a related quantitative discipline, or equivalent practical experience, with record of publications or open source tooling in these fields - Strong quantitative reasoning and statistical intuition - Demonstrated ability to identify important scientific questions, design rigorous investigations, and draw reliable conclusions from complex biological data. - Experience analyzing high throughput sequencing data (e.g. RNA-seq, functional genomics / transcriptomics, MPRA), with a focus on robust statistical analysis - Experience collaborating closely with AI/machine learning researchers or applying machine learning or generative AI tools to scientific problems - Familiarity with current AI/machine learning methods, including generative foundation models, representation learning, and model evaluation - Familiarity with publicly available biological datasets and data derived from high throughput assays - Capable programmer in Python and common scientific computing libraries - Excellent written and verbal communication skills, including the ability to explain complex findings to audiences with diverse technical backgrounds - Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET - Readiness to travel several times a year for company retreats and business events - We value the benefits of in-person collaboration and expect candidates to primarily work from our office locations Preferred technical skills - 3+ years of post-PhD experience in computational biology, biostatistics, machine learning research, or a related field Compensation $135K – $240K + Bonus + Equity What we offer - A competitive compensation package - 30 days paid vacation per year - Comprehensive health insurance for US based Beginners - 401K with company match for US based Beginners and Direktversicherung for German Beginners - Quarterly company-wide retreats - Monthly wellness benefit - Budget for multiple visits per year to our offices in Berlin, Palo Alto or Switzerland - Learning & Development budget to attend conferences, take courses, or otherwise invest in your professional growth - A buddy to help you get settled *Varies by country and does not apply to internships At Inceptive, we are creating tools to develop increasingly powerful biological software for the rational design of novel, broadly accessible medicines and biotechnologies previously out of reach. Our team brings together vast expertise in molecular biology, machine learning, and software engineering, and we are all working towards becoming antedisciplinary, meaning we deepen the knowledge we have in our area of expertise while also expanding our knowledge of completely new fields. We approach our goals with a Beginner's mind, humbly and with fresh eyes, and aim to become the pioneers of a new discipline rooted in biology as much as in deep learning, whose impact will be realized together with out-of-the-box thinkers in business and entrepreneurship, defying established categorizations. We are building a company culture centered around growth, learning, and discovery. We believe in humility and open-mindedness in how we approach each other, as well as problems we don't yet have solutions for. It is the policy of Inceptive to ensure equal employment opportunity without discrimination or harassment on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, age, disability, marital status, citizenship, national origin, genetic information, or any other characteristic protected by law. Inceptive prohibits any such discrimination or harassment. Inceptive is also committed to welcoming and providing accommodations to people with disabilities. Please let us know if you need any accommodations throughout your application process.
Applied AI Engineer, Video Agent
About HeyGen At HeyGen, our mission is to make visual storytelling accessible to all. Over the last decade, visual content has become the preferred method of information creation, consumption, and retention. But the ability to create such content, in particular videos, continues to be costly and challenging to scale. Our ambition is to build technology that equips more people with the power to reach, captivate, and inspire audiences. Learn more at www.heygen.com . Visit our Mission and Culture doc here . About the Video Agent team Video Agent turns a prompt, a document, or a handful of assets into a finished video. Behind a simple chat box sits a multi-step AI agent. It plans a storyboard, writes the script, chooses avatars, voices, and visuals, calls many tools along the way, and hands the result to our rendering pipeline. Customers use it every day to make training videos, product explainers, marketing content, and more. We are a small team that owns the agent end to end. That covers the prompts and tools the agent uses, the backend services that run it, and the product screens where people chat with it and edit what it makes. This is not a role where you only call someone else’s model through an API. HeyGen trains its own models in house, and the agent is built on top of them. You will work directly with those models and with the researchers who build them, which is an experience few applied AI roles offer. About the role You will make Video Agent better at doing what the user actually asked for, and you will prove it with data. Some weeks that means redesigning a tool the agent calls. Other weeks it means building an eval, running an A/B test, or shipping a new editing experience in React. You will own features from the first idea through launch and follow-up, and you will decide which problems belong to the model and which belong to plain, deterministic code. What you’ll do - Build the agent. Design the tools, prompts, and context the agent works with. Improve how it plans, how it recovers from mistakes, and how it handles long, multi-turn conversations. - Measure quality. Build evals and offline replays of real sessions. Run experiments behind feature flags and read the results honestly. Watch real user sessions and agent traces to find where things go wrong. - Ship across the stack. Build user-facing features in TypeScript and React. Build backend services and long-running workflows in Python and Go. - Work closely with others. Partner with product managers, designers, and the research and rendering teams that the agent depends on. What we’re looking for - You have built and shipped an AI agent that real people use. We mean a system where a model calls tools, plans across several steps, and acts on the results. A chat wrapper or a prompt demo is not enough on its own. We want to hear what broke in production and how you fixed it. - You know how to tell whether an AI system got better. You have built evals or test sets for model behavior. You can tell a real improvement from noise, and you are skeptical of results that look too good. - You are a strong full-stack engineer. You have 2 or more years of professional experience. You are fluent in TypeScript and React on the frontend and Python on the backend. - You have strong product sense. You use what you build, you care how it feels, and you look at how customers actually use it. - You take ownership. You do not wait for a spec. You find the most important problem, fix it, and tell people what changed. A degree in computer science or a related field is welcome but not required. Equivalent experience counts. Nice to have - You have fine-tuned models before. - You have worked on code-to-video models, which generate video by writing code such as HTML or animation scripts. - You have worked on video agent models in general. Show us an agent you’ve built With your application, please include a short note (a few sentences is fine) about an agent you have built. Tell us what it does, how you measured whether it worked, and one failure you found and fixed. A link to code, a demo, or a write-up is a bonus. Compensation & Benefits - $180,000 – $240,000 annually - Equity in a high-growth AI company. - 401K Match - Health, dental, and vision benefits. - Flexible PTO. - Opportunity to help define a new category in AI video. - High ownership, high velocity, and direct impact on revenue growth. Equal Opportunity HeyGen is an Equal Opportunity Employer. We value diversity and are committed to building an inclusive environment for all employees.
AI Research Engineer
ABOUT HEX Hex is growing our team of builders on a mission to make everyone a data person. Our platform solves key pain points with today’s data and analytics tooling, and empowers anyone to explore data using natural language, with or without code, on trusted context. Thousands of customers like Ramp https://hex.tech/customers/ramp/, Figma https://hex.tech/customers/figma/, Stubhub https://hex.tech/customers/stubhub/, Anthropic, and Gamma love Hex https://www.g2.com/products/hex-tech-hex/reviews for our beautiful UI, agentic superpowers, and boundless flexibility. Hex has raised over $100M from leading investors like Sequoia, a16z, Snowflake and Amplify. With office hubs in San Francisco and New York, as well as remote team members throughout the US and UK, we’re a high-agency team that ships fast and has fun doing it. Check out our Intro to Hex video https://www.youtube.com/watch?v=qAXsnrNlavk and employee handbook https://www.notion.so/hexhq/Hex-Handbook-9fff0d42860e4f70815c599e0d1664d6 to learn more about what it’s like to work at Hex. ABOUT THE ROLE Hex is an AI-powered platform for modern Data Science and Data Analytics workflows. AI Research Engineers at Hex partner with product teams to build industry-leading AI experiences such as the Notebook Agent https://hex.tech/blog/introducing-notebook-agent/. In the process, AI Research Engineers will run experiments, fine-tune models, deploy AI infrastructure, and build and maintain experimentation tooling. As an AI Research Engineer partnered with Hex's product teams, your focus will be driving Hex's context engine and pushing forward the capabilities of our Notebook Agent. Built for people doing the most complex and impactful data work in an organization, the Notebook Agent is a highly technical data copilot that writes SQL and python, builds beautiful reports and visualizations, and collaborates with the analyst on new avenues of inquiry. Whether its pushing the frontiers of SQL generation quality or using LLMs to evaluate agent threads, your job will be to enable the data teams that work in Hex to build out highly accurate and curated data experiences for their stakeholders, unlocking the power of data driven decisions for the rest of the business. If you’re a strong builder excited to bring these capabilities to thousands of users, come do it on the best Data Science platform around, with the best user context. We are looking for a senior engineer (from an AI Eng, SWE or MLE background) eager to rapidly expand our capabilities in several greenfield applications. As one of our team’s early hires, you will work on a variety of initiatives including: - Building features and experiences from 0 to 1 - Partnering on determining the architecture and stack for our AI-enabled capabilities - Shipping product experiences that fundamentally change the way that Data Scientists and Analysts operate - Working at the cutting edge of production AI applications This type of ownership will provide incredible opportunities for personal and professional growth. You will have ample technical and team leadership opportunities, depending on your interests. We aim to deliver AI capabilities that radically accelerate and augment the data science workflow – we’ve got an exciting roadmap and are willing to share more details during the process. We’re particularly interested in candidates who bring inspired excitement about what is possible in this space. WE'D LOVE TO HEAR FROM YOU IF YOU HAVE: - Experience getting AI/ML capabilities into production and serving real users – we’re not currently looking for new grad folks. - A lot of enthusiasm for applications of AI to real business problems. - Understanding of core MLOps/SW Architecture concepts for modern ML-based applications. - Ideal candidates are strong on Infra aspects of MLOps as well. - You’re comfortable working in both Python & JS/TS – it’s okay if you’re only strong in one, but openness to both is important. - Experimentalist mindset – understanding that quick iteration, and discerning critical thinking, can yield rapid progress on new challenges. - Interest in the data space, and a love of shipping great products and building tools that empower end users to do more. - Experience maintaining a high quality bar for design, correctness, and testing. OUR STACK Our product is a web-based notebook and app authoring platform. Our frontend is built with Typescript and React, using a combination of Apollo GraphQL and Redux for managing application state and data. On the backend, we also use Typescript to power an Express/Apollo GraphQL server that interacts with Postgres, Redis, and Kubernetes to manage our database and Python kernels. Our backend is tightly integrated with our infrastructure and CI/CD, where we use a combination of Terraform, Helm, and AWS to deploy and maintain our stack. This is a full-time role based out of either our San Francisco or New York office. Employees are encouraged to come into the office at least twice a week, with Tuesdays and Thursdays being our main in-person days. The salary range for this role is: $214,000 - $285,000 In addition to our unique culture, Hex proudly offers a competitive total rewards package, including but not limited to, market-benched salary & equity, comprehensive health benefits, and flexible paid time off. The salary range shown may be a reflection of additional factors such as geographical location and skill ranges/levels we’re open to. Placement in the salary range will be decided upon completion of the interview process, taking into account factors like leaving room for growth, internal fairness & parity, your demonstrated skills, and the depth of your experience. Our Recruiting team will be able to provide more details during the interview process. By submitting an application the candidate consents to the use of their personal information in accordance with the Hex Privacy policy: https://learn.hex.tech/docs/trust/privacy-policy.
Senior AI Models 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 We help enterprise customers build AI systems tailored to their specific work. Our goal is to deliver better economics than relying solely on closed-source models such as Claude, Gemini, and OpenAI. You will work across model customisation, deployment, agent systems, and customer delivery. We do not expect one person to specialise in everything. You should bring strong experience in at least two of these areas and be comfortable collaborating across the others. WHAT YOU WILL DO As a Senior, you will own system design, technical decisions and customer outcomes. Your responsibilities will include: Model customisation - Turn customer use cases into training and evaluation plans. - Select suitable data, models, and training approaches. - Fine-tune and evaluate models for specialised domains. - Improve quality based on testing and production feedback. Deployment and inference - Design cost-effective cloud or on-prem deployment approaches. - Select suitable infrastructure and serving configurations. - Improve reliability, latency, and operating cost. - Build monitoring, backup, and recovery processes. Agent systems - Build agent workflows around language models. - Connect models with customer data, tools, and existing systems. - Add the controls and observability needed for production use. Customer delivery - Work with customers to understand their requirements and constraints. - Explain trade-offs between quality, cost, privacy, and complexity. - Help take solutions from early discovery through production launch. WHAT WE WILL LOOK FOR - Strong experience in at least two of the areas above. - Practical experience building or operating production AI systems. - Solid software engineering or machine learning engineering skills. - Familiarity with open-weight language models and the modern LLM ecosystem. - Good judgement when balancing model quality, cost, latency, and reliability. - The ability to work through ambiguous technical and customer requirements. - Clear communication with both technical and non-technical stakeholders. 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. Heidi is dedicated to creating an equitable, inclusive, and supportive work environment that brings people together from diverse backgrounds, experiences, and perspectives. Our strength is in our differences. We're proud to be an equal opportunity employer and welcome all applicants as we're committed to promoting a culture of opportunity for all.
Mid Level AI Models 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 We help enterprise customers build AI systems tailored to their specific work. Our goal is to deliver better economics than relying solely on closed-source models such as Claude, Gemini, and OpenAI. You will work across model customisation, deployment, agent systems, and customer delivery. We do not expect one person to specialise in everything. You should bring strong experience in at least two of these areas and be comfortable collaborating across the others. WHAT YOU WILL DO As a mid level engineer you will be working independently and confidently owning delivery across: Model customisation - Turn customer use cases into training and evaluation plans. - Select suitable data, models, and training approaches. - Fine-tune and evaluate models for specialised domains. - Improve quality based on testing and production feedback. Deployment and inference - Design cost-effective cloud or on-prem deployment approaches. - Select suitable infrastructure and serving configurations. - Improve reliability, latency, and operating cost. - Build monitoring, backup, and recovery processes. Agent systems - Build agent workflows around language models. - Connect models with customer data, tools, and existing systems. - Add the controls and observability needed for production use. Customer delivery - Work with customers to understand their requirements and constraints. - Explain trade-offs between quality, cost, privacy, and complexity. - Help take solutions from early discovery through production launch. WHAT WE WILL LOOK FOR - Strong experience in at least two of the areas above. - Practical experience building or operating production AI systems. - Solid software engineering or machine learning engineering skills. - Familiarity with open-weight language models and the modern LLM ecosystem. - Good judgement when balancing model quality, cost, latency, and reliability. - The ability to work through ambiguous technical and customer requirements. - Clear communication with both technical and non-technical stakeholders. 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. Heidi is dedicated to creating an equitable, inclusive, and supportive work environment that brings people together from diverse backgrounds, experiences, and perspectives. Our strength is in our differences. We're proud to be an equal opportunity employer and welcome all applicants as we're committed to promoting a culture of opportunity for all.
Staff Software Engineer, AI Platform
WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW Harvey’s products all depend on a shared AI foundation: the model layer and agent infrastructure that determine the quality of work our agents deliver. Legal is one of the hardest domains for AI: documents run to hundreds of pages, matters can span millions of files, and there is zero margin for error on accuracy. The AI Platform team builds the foundation that every product and agent team at Harvey builds upon. This team is early and there’s a lot to build: model routing, agent architecture, context management, evals. Your work here sets the ceiling for what Harvey’s AI can do. REPRESENTATIVE PROJECTS - Context Engineering & Agent Infrastructure. Build the platform-level systems for context management, session state, and memory that all of Harvey’s agents and products rely on. - Model Integration & Routing. Own the infrastructure that lets Harvey onboard new foundation models fast and route to the right one for every task - a capability every product team depends on. - Evaluation Infrastructure. Build the shared eval tooling and frameworks that let every team across Harvey measure and improve AI quality systematically. - Shared Abstractions. Create the SDKs, platform primitives, and developer tooling that make it dramatically easier for product teams to ship AI-powered features. WHAT YOU’LL DO - Design and build abstractions and platform-level systems that improve all of Harvey’s agentic products. - Own infrastructure for model integration, routing, and evaluation that helps Harvey choose and deploy the right foundation model for any given context. - Build evaluation frameworks and tooling that let every team across Harvey iterate on AI quality effectively. - Partner closely with product engineering teams, PMs, and design to launch cutting-edge AI products. - Evaluate, prototype, and integrate the latest advancements in AI and agentic systems as they emerge. WHAT YOU HAVE - 8+ years of experience building backend systems, with at least 1+ year focused on AI/ML engineering and a track record of technical leadership across teams. - Experience building and shipping multi-model or multi-provider AI systems in production. - Familiarity with context management, session state, or memory systems in AI or distributed systems. You’ve thought about what the model sees and why it matters. - A track record of building internal platforms, SDKs, or shared infrastructure that other engineering teams actually adopted - and an understanding of why developer experience matters as much as raw capability. - Strong judgment about abstractions. Opinionated about good design but pragmatic about shipping incrementally. - Excitement about agentic AI and the infrastructure challenges of making autonomous systems reliable when the stakes are real. - A bias toward full ownership: you navigate ambiguity well and don’t wait for a roadmap to start solving problems. - Bonus: experience building evaluation frameworks, working with agent/function-calling architectures, familiarity with legal or other high-stakes professional services domains, or time at early-stage or hyper-growth startups where the underlying technology changes regularly. COMPENSATION RANGE $231,000 - $340,000 DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES [HERE https://www.notion.so/harveyai/Harvey-Candidate-Privacy-Policies-319ac3fcdd7a803bb807d5094f249922]. #LI-AK1 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai
Senior Software Engineer, AI Platform
WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW Harvey’s products all depend on a shared AI foundation: the model layer and agent infrastructure that determine the quality of work our agents deliver. Legal is one of the hardest domains for AI: documents run to hundreds of pages, matters can span millions of files, and there is zero margin for error on accuracy. The AI Platform team builds the foundation that every product and agent team at Harvey builds upon. This team is early and there’s a lot to build: model routing, agent architecture, context management, evals. Your work here sets the ceiling for what Harvey’s AI can do. REPRESENTATIVE PROJECTS - Context Engineering & Agent Infrastructure. Build the platform-level systems for context management, session state, and memory that all of Harvey’s agents and products rely on. - Model Integration & Routing. Own the infrastructure that lets Harvey onboard new foundation models fast and route to the right one for every task - a capability every product team depends on. - Evaluation Infrastructure. Build the shared eval tooling and frameworks that let every team across Harvey measure and improve AI quality systematically. - Shared Abstractions. Create the SDKs, platform primitives, and developer tooling that make it dramatically easier for product teams to ship AI-powered features. WHAT YOU’LL DO - Design and build abstractions and platform-level systems that improve all of Harvey’s agentic products. - Own infrastructure for model integration, routing, and evaluation that helps Harvey choose and deploy the right foundation model for any given context. - Build evaluation frameworks and tooling that let every team across Harvey iterate on AI quality effectively. - Partner closely with product engineering teams, PMs, and design to launch cutting-edge AI products. - Evaluate, prototype, and integrate the latest advancements in AI and agentic systems as they emerge. WHAT YOU HAVE - 5+ years of experience building backend systems, with at least 1+ year focused on AI/ML engineering. Staff candidates will typically have 8+ years and a track record of technical leadership across teams. - Experience building and shipping multi-model or multi-provider AI systems in production. - Familiarity with context management, session state, or memory systems in AI or distributed systems. You’ve thought about what the model sees and why it matters. - A track record of building internal platforms, SDKs, or shared infrastructure that other engineering teams actually adopted - and an understanding of why developer experience matters as much as raw capability. - Strong judgment about abstractions. Opinionated about good design but pragmatic about shipping incrementally. - Excitement about agentic AI and the infrastructure challenges of making autonomous systems reliable when the stakes are real. - A bias toward full ownership: you navigate ambiguity well and don’t wait for a roadmap to start solving problems. - Bonus: experience building evaluation frameworks, working with agent/function-calling architectures, familiarity with legal or other high-stakes professional services domains, or time at early-stage or hyper-growth startups where the underlying technology changes regularly. COMPENSATION $220,000 - $300,000 DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES [HERE https://www.notion.so/harveyai/Harvey-Candidate-Privacy-Policies-319ac3fcdd7a803bb807d5094f249922]. Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai
Engineering Manager, Model Infrastructure
WHY HARVEY At Harvey, we’re transforming how legal and professional services operate. By combining frontier agentic AI, an enterprise-grade platform, and deep domain expertise, we’re reshaping how critical knowledge work gets done for decades to come. This is a rare chance to help build a generational company at a true inflection point. We have strong product-market fit and world-class investor support. We’re scaling fast and defining a new category in real time. The work is ambitious, the bar is high, and the opportunity for growth — personal, professional, and financial — is unmatched. Our team moves fast, takes ownership, and is deeply committed to the mission — operating with intensity, staying close to our customers, and pushing each other for excellence. We live by three values: Decisiveness, Simplicity, and Job's Not Finished. We act quickly on clear judgment over perfect information, we believe simplicity is what scales, and we're never satisfied with where we are. If you want to do the best work of your career alongside people who share that drive, we'd love to build with you. At Harvey, the future of professional services is being written today — and we’re just getting started. ROLE OVERVIEW As the Engineering Manager for Model Infrastructure, you'll lead the team responsible for the platform powering every model request across Harvey. You'll partner closely with AI Research, Product Engineering, Infrastructure, and external AI providers to ensure our platform remains reliable, scalable, and cost-efficient as our business grows. Model Infrastructure is one of Harvey's most strategic engineering organizations. Every product capability—from chat experiences and agents to document workflows and future reasoning systems—depends on this platform. Over the next several years, the team will evolve beyond operating third-party models to building the infrastructure that enables Harvey to train, evaluate, deploy, and operate our own frontier AI models. This role offers the opportunity to shape the technical foundation of Harvey's AI platform and build an organization that will power the company's next phase of growth. WHAT YOU'LL DO - Lead and grow a high-performing team of software engineers responsible for Harvey's Model Infrastructure platform. - Define the technical roadmap for model reliability, scalability, and operational excellence. - Build highly reliable systems for model provisioning, capacity management, failover, and incident response across multiple AI providers. - Own Harvey's multi-provider model platform, including provider integrations, SDK upgrades, API migrations, and onboarding new model providers. - Drive the evolution of our Unified Model Controller (UMC) and Model Selector platform to automatically detect degraded models and intelligently route traffic based on health, latency, quality, compliance, and cost. - Improve observability through health dashboards, alerting, token usage analytics, cost reporting, and end-to-end model telemetry. - Partner with Product Engineering to support new model launches, capacity planning, experimentation, and proactive production monitoring. - Lead initiatives to improve inference efficiency, reduce infrastructure costs, and increase model utilization across providers. - Build the infrastructure foundation for Harvey's future model training efforts, including data pipelines, model operations, training environments, and AI platform capabilities. - Partner with executive leadership on long-term AI infrastructure strategy and vendor relationships. - Recruit, mentor, and develop exceptional engineering talent while fostering a culture of technical excellence and operational ownership. WHAT YOU HAVE - 8+ years of software engineering experience, including multiple years managing high-performing engineering teams. - Experience leading teams responsible for large-scale distributed systems or cloud infrastructure. - Strong technical background that enables you to guide architectural decisions and mentor senior engineers. - Experience operating highly available production services with strong reliability and operational excellence. - Experience building platforms that require scalability, observability, automation, and cost optimization. - Strong cross-functional leadership skills with the ability to partner effectively across Engineering, Research, Product, and external vendors. - Excellent communication skills and the ability to influence technical strategy across organizations. - A passion for building teams and developing engineering talent. NICE TO HAVE - Experience with AI infrastructure, LLM serving, or machine learning platforms. - Experience working with multiple model providers such as OpenAI, Anthropic, Azure OpenAI, Fireworks, Baseten, or open-source model ecosystems. - Experience building inference platforms, model gateways, traffic routing systems, or policy-based serving infrastructure. - Experience with Kubernetes, cloud infrastructure, distributed systems, and large-scale observability platforms. - Experience supporting GPU infrastructure, model training platforms, or ML infrastructure. - Familiarity with data platforms and technologies such as Spark, Kafka, Flink, Airflow, or Iceberg. - Experience leading organizations through periods of rapid growth and technical transformation. COMPENSATION $260,000 - $340,000 USD DEPENDING ON YOUR LOCATION, AN APPLICANT PRIVACY NOTICE MAY APPLY TO YOU. YOU CAN FIND ALL OF OUR APPLICANT PRIVACY NOTICES HERE https://harveyai.notion.site/Harvey-Candidate-Privacy-Notices-319ac3fcdd7a803bb807d5094f249922?pvs=74. #LI-AN2 Harvey is an equal opportunity employer and does not discriminate on the basis of race, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition, or any other basis protected by law. We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made by emailing accommodations@harvey.ai
Software Engineer, ML Systems
ABOUT HARMONIC At Harmonic, we are building a mathematical reasoning engine that operates with absolute precision. While most AI makes maximum-likelihood guesses, Harmonic's Aristotle uses Lean 4 and reinforcement learning to verify its reasoning and results. Following our Gold Medal-level performance on the 2025 International Math Olympiad (IMO) and the successful resolution of long-standing open problems, we are proving that AI can master the most rigorous domains of human thought. Backed by some of the world’s most prominent investors, we are intentionally scaling an elite technical team. Visit our company blog https://harmonic.fun/news to learn more about what we are working on! ABOUT THE ROLE We are looking for a pragmatic, Software Engineer to own the productionization of our research pipelines. This is an implementation-heavy role designed for an engineer who can take a nascent research idea and build the robust, scalable machinery required to prove it at scale within our cloud infrastructure. KEY RESPONSIBILITIES - Pipeline Engineering: Build and manage end-to-end ML pipelines (ETL and automated evaluation) that are the bedrock of our RL research. - Bottleneck Resolution: Identify and refactor inefficient research code. You act as the primary engineer ensuring that a promising idea reaches its full potential through scalable code. - Standardization: Establish best practices for versioning, experiment tracking, and CI/CD for ML models to ensure reliability. - Cloud Infrastructure & Observability: Manage the deployment and scaling of workloads on Kubernetes. Implement the tooling and telemetry that allows the team to understand agent behavior and training health at a glance. MINIMUM QUALIFICATIONS - BS in Computer Science, a related technical field, or equivalent industry experience - 2+ years of relevant industry experience - Expert-level Python skills and a disciplined approach to software engineering (testing, versioning, and modular design). - Experience building and managing end-to-end ML pipelines in a production or research-intensive environment. PREFERRED QUALIFICATIONS - Full-stack ML experience: Comfortable moving from data engineering to model debugging. - Experience refactoring research-grade code into high-quality, scalable production packages. - Proven ability to design and implement complex data-loading and evaluation systems for non-deterministic models. - Experience with workflow orchestration tools (e.g., Kubeflow, Airflow, or Metaflow). - Experience managing large-scale experiments on cloud providers (AWS, GCP, or Azure). - Proven track record collaborating directly with researchers to translate algorithmic requirements into engineering roadmaps. - Hands-on experience with containerization (Docker) and orchestration (Kubernetes). WHAT WE OFFER - Unlimited PTO - 401(k) matching - 100% employer-paid health, vision, and dental benefits for employees and 50% coverage for dependents. Harmonic offers varied health coverage options to select what is best for you and your family. - Health Savings Account (HSA) available for qualifying health plans EQUAL OPPORTUNITY STATEMENT Harmonic is committed to diversity and inclusivity in the workplace. We are an equal opportunity employer and do not discriminate on the basis of race, religion, national origin, gender, sexual orientation, age, veteran status, disability or any other legally protected status.
Senior Software Engineer, Backend Platform
ABOUT HANDSHAKE Handshake was founded on a simple belief that everyone deserves a path to a great career, regardless of where they went to school or who they know. Today, we power 25 million job seekers, 1 million+ employers, and 1,600 educational institutions. In 2025, we started Handshake AI and built the fastest-growing AI data business in history. We work directly with frontier AI lab researchers to create evaluations, publish benchmarks, and push the boundary of data. We’ve grown from $0 to ~$1B run rate and pay ~$60M to over 30K individuals every month. Why join Handshake now: - Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel - Partner hand-in-hand with world-class AI labs, Fortune 500 partners and the world’s top educational institutions - Work together with engineers, scientists, operators, and more from Palantir, Meta, Scale AI, and former YC founders - Build a massive, fast-growing business with billions in revenue About Handshake AI Human data is the core infrastructure to AI advancement. Frontier AI labs currently improve model capabilities with various data-intensive post-training techniques. We believe that data spend for AI training will increase by 3-5x in the next few years and continue for much longer as models take on new domains. Handshake AI supports all of the frontier AI labs, working on their most complex data at the largest scale. THE ROLE Handshake is hiring a Senior Software Engineer to join Backend Platform, with an initial focus on supporting Handshake AI (HAI) and internal AI initiatives. You will build and maintain backend services, shared platform capabilities, and developer tooling that support HAI and internal AI initiatives. The work centers on core backend platform concerns—service architecture, APIs, data systems, performance, reliability, and developer experience—in an area making increasing use of LLMs and agents. We are looking for someone who combines strong backend fundamentals with practical knowledge of LLM-powered and agentic systems. WHAT YOU'LL DO - Design, build, and operate backend services and APIs that support HAI products and internal AI initiatives, primarily within our full-stack TypeScript repository. - Develop shared libraries, services, and architectural patterns that reduce duplicated work and make backend systems easier to extend and operate. - Improve the performance, scalability, reliability, and observability of HAI's backend systems. - Lead work involving database design, data access patterns, query performance, indexing, caching, and the evolution of data models as products grow. - Identify recurring product-engineering problems and turn the right ones into reusable platform capabilities and well-understood patterns. - Make sound architectural decisions across mature systems and new services, balancing delivery speed, performance, maintainability, and operational cost. - Contribute directly to internal AI initiatives, including shared capabilities used to build, evaluate, and operate LLM-powered and agentic systems. - Partner with other teams in our Developer Platform group, including ML and Cloud Engineering, on model and service integration, orchestration, evaluation, observability, and failure handling. - Participate in technical planning, design and code reviews, operational support, and incident response. - Mentor other engineers and help establish practical backend engineering standards across HAI and internal AI initiatives. YOU HAVE - 5+ years of professional software engineering experience, primarily in backend or platform engineering. - Experience designing, building, and operating production services and APIs. - Strong understanding of distributed systems, data modeling, service boundaries, common backend architecture patterns, and reliability and performance tradeoffs. - We strongly prefer candidates with deep TypeScript and Next.js experience. Because this work is primarily based in a full-stack TypeScript repository, you should be comfortable navigating the broader application and contributing across boundaries when necessary. - Strong experience with relational databases, including schema design, query optimization, indexing, transactions, and diagnosing database-related performance problems. Experience with PostgreSQL is especially relevant. - Familiarity with backend technologies such as Temporal, Redis, Elasticsearch, GraphQL, or event-driven systems. - Experience operating services in cloud and containerized environments. Our broader platform uses technologies including GCP, Kubernetes, Terraform, and Docker. - Practical knowledge of LLM-powered systems, including concepts such as model and tool integration, RAG, agent orchestration, prompt management, evaluation, and observability. - A record of independently driving ambiguous projects from problem definition through production rollout. - Strong communication skills and the ability to collaborate with product engineers and other teams across Developer Platform. - Sound technical judgment and a preference for understandable, maintainable systems over unnecessary complexity. BONUS POINTS - Direct production experience with product features powered by ML, LLMs, or agents. - Experience with LLM orchestration frameworks or agent architectures involving tool use, memory, and multi-step workflows. - Experience building internal AI tools or shared AI capabilities used by other engineers. - Experience creating internal platforms, paved paths, or self-service tools used by other engineers. - Experience modernizing mature backend systems while continuing to support active product development. - Experience with event-driven architecture or high-throughput data systems. HOW WE THINK ABOUT THIS ROLE This is a backend platform role supporting HAI and internal AI initiatives. The strongest candidates will bring TypeScript and Next.js depth alongside experience in backend architecture, databases, and performance, plus enough practical AI knowledge to contribute directly to this work. Success does not require expertise in every model or framework, but it does require the ability to design and operate the systems around AI-powered product experiences. WE OFFER Handshake delivers benefits that help you feel supported — and thrive at work and in life. The below benefits are for full-time US employees. 🎯 Ownership: Equity in a fast-growing company 💰 Financial Wellness: 401(k) match, competitive compensation, financial coaching 🍼 Family Support: Paid parental leave, fertility benefits, parental coaching 💝 Wellbeing: Medical, dental, & vision, mental health support, $500 wellness stipend 📚 Growth: $2,000 learning stipend, ongoing development 💻 Remote & Office: Internet, commuting, & free lunch/gym in our SF office 🏝 Time Off: Flexible PTO, 15 holidays + 2 flex days 🤝 Connection: Team outings & referral bonuses Explore our mission, values, & comprehensive US benefits at joinhandshake.com/careers http://joinhandshake.com/careers.
Software Engineer, Machine Learning
About Glean: Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more - deeply embedded where people get things done. You’ll ship agentic capabilities on an open, extensible stack, with the craft and care required for enterprise trust, as we bring Work AI to every employee, in every company. About the Role: Glean is looking for engineers to help build the world’s best search and assistant product for work. Our engineers work on a range of systems across the stack, including query understanding, document understanding, domain-adapted language models, natural language question-answering, evaluation, and experimentation. We interact regularly with customers, deeply understand their pain points, and use whatever tool is necessary, simple or complex, to solve their problems. You will: - Invent new signals to improve the personalization of our search engine - Train a model to capture interactions between signals in our ranking system - Design smarter ways to domain-adapt language models to each customer’s corpus - Discover new ways of combining LLMs with search engines to answer complex questions - Write robust code that’s easy to read, maintain, and test - Mentor more junior engineers, or learn from battle-tested ones About you: - 6+ years of experience - BA/BS in computer science, math, sciences, or a related degree - Experience working with search, recommendation, natural language processing, or other large systems involving machine learning - Strong analytical skills and ability to work with data - Proven ability to design, build, and ship production-ready models - Proficiency in your ML framework of choice - Strong coding skills (Python, Go, Java, C++, ...) - Thrive in a customer-focused, tight-knit and cross-functional environment - being a team player and willing to take on whatever is most impactful for the company is a must - A proactive and positive attitude to lead, learn, troubleshoot and take ownership of both small tasks and large features Location: - This role is in person in Bangalore, India Compensation & Benefits: Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits. We are a diverse bunch of people and we want to continue to attract and retain a diverse range of people into our organization. We're committed to an inclusive and diverse company. We do not discriminate based on gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race. AI-First Mindset at Glean: At Glean, AI fluency is core to how we work and we're committed to ensuring every new hire feels confident integrating AI into their everyday work. As part of the interview process, you'll complete a brief AI-focused exercise or discussion so we can understand how you think about, design, and use AI to drive impact in your role. Feel free to reference any tools, platforms, or workflows you use today — prior Glean experience isn't required. Global Data Privacy Notice for Job Candidates and Applicants: Depending on your location, the General Data Protection Regulation (GDPR), California Consumer Privacy Act (CCPA), or other privacy laws may regulate the way we manage the data of job applicants. Our full notice outlining how data will be processed as part of the application procedure for applicable locations is available in our Privacy Policy . By submitting your application, you are agreeing to our use and processing of your data as required. US applicants and their applications are subject to arbitration of disputes as outlined in our Applicant Arbitration Agreement . By clicking “Submit Application,” I confirm that I have read the Global Data Privacy Notice and the Applicant Arbitration Agreement , and I agree to the terms.
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