Browse AI Jobs — Page 121

By Anduril IndustriesJan 7, 2026

Mission Software Engineer, Vehicle Software, Compu...

Negotiable

Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. ABOUT THE TEAM Air Dominance & Strike (AD&S) is responsible for autonomous robotics systems like the Fury unmanned fighter jet and the Barracuda family of advanced effects. The AD&S Vehicle Software team is responsible for the software running on these systems. Our software engineers collaborate with other engineering disciplines to develop software for vehicle control, networking, sensor integration, and telemetry. We are looking for engineers excited to build the foundational vehicle software stack that supports the wide range of AD&S initiatives, from early concept simulation to first flight to live operations to large scale fleet management. ABOUT THE JOB We are seeking a software engineer specialized in machine vision for development of guidance systems on a large air vehicle. Engineers in this role will primarily work on implementing novel image processing algorithms from various image sources and sensors. These will be both conventional and state of the art machine learning approaches. Additionally, the role requires the ability to work with other sensor sources and develop sensor fusion algorithms. WHAT YOU’LL DO - Integrate and write tests for camera calibration code - Develop and refine algorithms for real-time image processing and analysis. - Write, test, and maintain high-quality code in Python and C++. - Optimize software for performance, reliability, and scalability - Process and evaluate images from live tests used by camera systems in various flight conditions. - Debug and troubleshoot software and hardware issues as they arise. - Propose and implement improvements to enhance system performance and capabilities REQUIRED QUALIFICATIONS - BS, MS, or PhD in Computer Science, Electrical Engineering, Mathematics, Physics, or related field. - 4+ years of experience writing production software (C or C++ preferred). - Direct experience with camera systems, calibration, and pose estimation. - Experience working with IMUs and implementing state fusion algorithms. - A desire to work on critical software that has a real-world impact - Eligible to obtain and maintain a U.S. Top Secret security clearance PREFERRED QUALIFICATIONS - Experience with SLAM systems. - Experience with estimation from RADAR or LIDAR systems. - General exposure to aviation concepts and aircraft design principles. - Experience working in airgapped environments (without network connectivity) - Knowledge of defense technology and tactics, especially in the air domain - Awareness of safety critical software development practices, including DO-178c and/or MIL-HDBK-516C. US Salary Range $220,000 - $292,000 USD The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including: Benefits At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits . Protecting Yourself from Recruitment Scams Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information. To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind: - No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates. - Please always verify communications: - Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. - Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com . - Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links. - What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com . Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts. Data Privacy To view Anduril's candidate data privacy policy, please visit https://anduril.com/applicant-privacy-notice/ . By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks as part of your application process. This third-party service provider provides risk-intelligence services that may include analysis of sanctions and watchlists, adverse media, public-record information, and other lawful open-source or commercial data sources. This third-party service provider does not act as a consumer reporting agency. Use of this provider helps to ensure compliance with applicable laws and protect technology, intellectual property, and organizational security.

👤 HumanFull-timeMid

By Anduril IndustriesMay 14, 2025

Senior Research Scientist, Manipulation

Negotiable

Negotiable

You'll build the systems that make reinforcement learning work at frontier scale — coupling policy optimization with large fleets of inference workers, agentic environments, and the reward and verification systems that turn model behavior into learning signal. RL is how Luma's models go from capable to useful. RL at scale is a full-loop systems problem: training, rollout generation, environment execution, and reward computation running concurrently across thousands of GPUs, all needing to stay fast, stable, and correct together. It fits someone who has lived this — post-trained LLMs with RL, built environments and verifiers, and debugged asynchronous rollout pipelines at scale. If you haven't operated RL at real scale, this will be deep water. What You'll Own - Design, build, and scale distributed RL post-training systems, orchestrating trainer, rollout, environment, and reward workloads across thousands of GPUs. - Build high-throughput rollout generation, integrating inference engines (vLLM, SGLang), weight synchronization, and asynchronous/off-policy schemes. - Design RL environments for agentic, multi-step tasks — sandboxed code execution, tool use, computer use, multimodal interaction — reproducible and scalable to millions of episodes. - Build reward infrastructure: verifiable/programmatic rewards, reward-model serving, LLM-as-judge pipelines, and defenses against reward hacking. - Develop the evaluation, monitoring, and debugging tooling that keeps large RL runs stable. - Advance training efficiency and stability, and turn new post-training ideas into production runs with researchers. First 90 Days One way the first 90 could unfold. - Days 1–30 — Immerse & Diagnose: Learn the current RL stack and where throughput, stability, or correctness break. - Days 30–60 — Ship & Validate: Improve a piece of the loop (rollout throughput, reward infra, or an environment) and prove it on a real run. - Days 60–90 — Scale & Systemize: Harden the full loop across thousands of GPUs and asynchronous architectures. What You Bring - Hands-on experience post-training LLMs with RL (PPO/GRPO-family, RLHF, RLVR) at meaningful scale. - Extensive distributed PyTorch training and parallelism (FSDP, Tensor/Pipeline/Expert Parallel) for foundation models. - Experience building RL environments, reward functions, verifiers, or evaluation harnesses for LLM agents, including sandboxed execution and multi-turn tool use. - Deep familiarity with RL post-training frameworks (veRL, OpenRLHF, TRL, Ray orchestration) and rollout inference engines (vLLM, SGLang). - Strong understanding of GPU clusters, networking, and communication libraries (NCCL, MPI) under mixed training and inference workloads. Nice to Have - Running RL training across 100+ GPUs, including asynchronous or disaggregated trainer/rollout architectures. - Containerization and orchestration (Kubernetes, Ray) for large environment fleets and sandboxed workloads. - Research contributions in RL for LLMs, or open-source contributions to RL training frameworks. 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.

👤 HumanFull-timeMid

By Luma AIJul 24, 2026

Research Scientist - World Model

Negotiable

You'll turn Luma's industry-leading generative video models into world models: interactive, controllable, physically faithful, and useful as a substrate for embodied reasoning. This is the role at the center of the thesis. You'll invent next-generation world-model architectures and the controllability that lets an agent step into a generated world, and own the metrics that define success. It fits a researcher with deep generative-modeling or model-based-RL expertise who has trained models to the limits of a multi-node cluster. If you want a narrow, well-scoped research problem, this is broader and more open-ended than that. What You'll Own - Invent next-generation world-model architectures (diffusion, transformer, autoregressive, or hybrid), focused on controllability and physical consistency. - Develop controllability mechanisms — action conditioning, view conditioning, long-horizon rollouts — that let an agent step into the world. - Define and own the metrics: physical fidelity, long-horizon coherence, action-following, and downstream usefulness for policy training. - Run scaling studies that show where compute, data, and architecture pay off. - Publish at the frontier and contribute to the open-source release that is the long-term deliverable. First 90 Days One way the first 90 could unfold. - Days 1–30 — Immerse & Diagnose: Get deep on the current video models and where they fall short as world models. - Days 30–60 — Ship & Validate: Prototype a controllability mechanism or architecture change and measure it against physical-fidelity and action-following metrics. - Days 60–90 — Scale & Systemize: Run scaling studies and push the most promising direction toward the open release. What You Bring - PhD or equivalent research record in ML, computer vision, robotics, or a related field. - Deep expertise in at least one of: large-scale generative modeling (video/3D/world), self-supervised representation learning, or model-based RL. - Strong PyTorch and large-scale training experience, to the limits of a multi-node cluster. - A research record the field knows (top-venue publications and/or widely used open releases). Nice to Have - Prior work on world models, model-based RL, generative video, neural simulation, or 4D scene representations. - Experience using generative models for downstream embodied tasks (planning, control, evaluation). - Enthusiasm for open-sourcing frontier models. 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.

👤 HumanFull-timeMid

By Luma AIJul 24, 2026

Open-Source Machine Learning Engineer - US Remote

Negotiable

At Hugging Face, we're on a journey to democratize good AI. We are building the fastest growing platform for AI builders with over 11 million users who collectively shared over 4 million models, 1 million datasets & 1.5 million Gradio apps. Our open-source libraries have more than 700,000 stars on Github. About the Role As an Open-Source Machine Learning Engineer, you'll work to improve the open-source machine learning ecosystem. You'll mainly work on existing open-source libraries such as Transformers, Datasets, Pytorch and vLLM, and you'll interact with users and contributors across the broad open-source ML ecosystem. We'll brainstorm with you to put you in a position to do the work that interests you and that is impactful. You'll help foster one of the most active machine learning communities, helping users contribute to and use the tools you build. You'll work with researchers, ML practitioners, and data scientists every day through GitHub, our forums, and Slack. About You You have a public track record of open-source work, and you enjoy collaborating with a community out in the open on GitHub. You love open source, you're passionate about making complex technology more accessible, and you want to contribute to one of the fastest-growing ML ecosystems. If that's you, we can't wait to see your application. What you'll need Strong Python skills, with experience writing clean, well-tested, maintainable library code Deep hands-on experience with a modern deep-learning framework, especially PyTorch (JAX or TensorFlow a plus) Practical experience with the Hugging Face open-source stack (Transformers, Datasets, Accelerate) or comparable ML libraries A public track record of open-source contributions, for example merged pull requests to ML or data libraries, that we can review on GitHub Solid understanding of modern machine learning and deep learning, including transformer architectures Experience collaborating with a technical community in the open (GitHub issues and reviews, forums, Slack or Discord) Fluent written English for asynchronous collaboration across a distributed, global community Nice to have Experience maintaining an open-source project Prior contributions to Transformers, Datasets, Accelerate, or similar libraries Familiarity with distributed training, inference optimization, or GPU/accelerator performance work Experience training or fine-tuning models at scale A note on fit If you're interested in joining us but don't tick every box above, we still encourage you to apply. We're building a diverse team whose skills, experiences, and backgrounds complement one another, and we're happy to consider where you might make the biggest impact. One more thing At Hugging Face we believe great AI shouldn't require a massive cluster, we build for everyone, especially the GPU-poor. And because we read every application, here's a small sign that you read this one too: start your answer to the first application question with the words “ GPU-poor and proud 🤗”. No trick, no catch, it just tells us a real person is on the other side. More about Hugging Face We are actively working to build a culture that values diversity, equity, and inclusivity . We are intentionally building a workplace where people feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community. Hugging Face is an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We value development. You will work with some of the smartest people in our industry. We are an organization that has a bias for impact and is always challenging ourselves to continuously grow. We provide all employees with reimbursement for relevant conferences, training, and education. We care about your well-being. We offer flexible working hours and remote options. We offer health, dental, and vision benefits for employees and their dependents. We also offer parental leave and flexible paid time off. We support our employees wherever they are . While we have office spaces in NYC and Paris, we're very distributed and all remote employees have the opportunity to visit our offices. If needed, we'll also outfit your workstation to ensure you succeed. We want our teammates to be shareholders . All employees have company equity as part of their compensation package. If we succeed in becoming a category-defining platform in machine learning and artificial intelligence, everyone enjoys the upside. We support the community . We believe major scientific advancements are the result of collaboration across the field. Join a community supporting the ML/AI community. Requirements Please provide a cover letter mentioning why you would like to work in open-source at Hugging Face. We encourage you to mention your skills, potential expertise, and topics on which you would like to work.

👤 HumanFull-timeMid

By Hugging FaceMay 29, 2026

Open-Source Machine Learning Engineer - EMEA Remot...

Negotiable

At Hugging Face, we're on a journey to democratize good AI. We are building the fastest growing platform for AI builders with over 11 million users who collectively shared over 4 million models, 1 million datasets & 1.5 million Gradio apps. Our open-source libraries have more than 700,000 stars on Github. About the Role As an Open-Source Machine Learning Engineer, you'll work to improve the open-source machine learning ecosystem. You'll mainly work on existing open-source libraries such as Transformers, Datasets, Pytorch and vLLM, and you'll interact with users and contributors across the broad open-source ML ecosystem. We'll brainstorm with you to put you in a position to do the work that interests you and that is impactful. You'll help foster one of the most active machine learning communities, helping users contribute to and use the tools you build. You'll work with researchers, ML practitioners, and data scientists every day through GitHub, our forums, and Slack. About You You have a public track record of open-source work, and you enjoy collaborating with a community out in the open on GitHub. You love open source, you're passionate about making complex technology more accessible, and you want to contribute to one of the fastest-growing ML ecosystems. If that's you, we can't wait to see your application. What you'll need Strong Python skills, with experience writing clean, well-tested, maintainable library code Deep hands-on experience with a modern deep-learning framework, especially PyTorch (JAX or TensorFlow a plus) Practical experience with the Hugging Face open-source stack (Transformers, Datasets, Accelerate) or comparable ML libraries A public track record of open-source contributions, for example merged pull requests to ML or data libraries, that we can review on GitHub Solid understanding of modern machine learning and deep learning, including transformer architectures Experience collaborating with a technical community in the open (GitHub issues and reviews, forums, Slack or Discord) Fluent written English for asynchronous collaboration across a distributed, global community Nice to have Experience maintaining an open-source project Prior contributions to Transformers, Datasets, Accelerate, or similar libraries Familiarity with distributed training, inference optimization, or GPU/accelerator performance work Experience training or fine-tuning models at scale A note on fit If you're interested in joining us but don't tick every box above, we still encourage you to apply. We're building a diverse team whose skills, experiences, and backgrounds complement one another, and we're happy to consider where you might make the biggest impact. One more thing At Hugging Face we believe great AI shouldn't require a massive cluster, we build for everyone, especially the GPU-poor. And because we read every application, here's a small sign that you read this one too: start your answer to the first application question with the words “ GPU-poor and proud 🤗”. No trick, no catch, it just tells us a real person is on the other side. More about Hugging Face We are actively working to build a culture that values diversity, equity, and inclusivity . We are intentionally building a workplace where people feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community. Hugging Face is an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We value development. You will work with some of the smartest people in our industry. We are an organization that has a bias for impact and is always challenging ourselves to continuously grow. We provide all employees with reimbursement for relevant conferences, training, and education. We care about your well-being. We offer flexible working hours and remote options. We offer health, dental, and vision benefits for employees and their dependents. We also offer parental leave and flexible paid time off. We support our employees wherever they are . While we have office spaces in NYC and Paris, we're very distributed and all remote employees have the opportunity to visit our offices. If needed, we'll also outfit your workstation to ensure you succeed. We want our teammates to be shareholders . All employees have company equity as part of their compensation package. If we succeed in becoming a category-defining platform in machine learning and artificial intelligence, everyone enjoys the upside. We support the community . We believe major scientific advancements are the result of collaboration across the field. Join a community supporting the ML/AI community. Requirements Please provide a cover letter mentioning why you would like to work in open-source at Hugging Face. We encourage you to mention your skills, potential expertise, and topics on which you would like to work.

👤 HumanFull-timeMid

By Hugging FaceMay 29, 2026

Applied Machine Learning Engineer, EMEA

Negotiable

ABOUT US: Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. THE ROLE: As an Applied Machine Learning Engineer, you are the technical owner of a customer engagement. This is what is globally known as the Forward Deployed Engineer role; at Fireworks in EMEA it carries the AMLE title. You embed with a small number of accounts, work in their environment, and own whatever stands between them and production on Fireworks: the model, the serving path, the integration, the performance. You scope the work before signature and deliver what you scoped. Depth over breadth: few accounts, owned end to end. What we value most is first principles thinking: reasoning about unfamiliar problems from the ground up rather than from playbooks. You will be evaluated on this directly in the interview process, and strength here can outweigh gaps elsewhere in your background. FOUR SPIKES: 1. Software engineering. You ship production systems and are comfortable dropped into an unfamiliar codebase or a customer environment with a deadline. Backend and systems depth, strong Python plus at least one systems language, and fluency with AI-assisted and agentic engineering as a core part of how you build. 2. Machine learning. You take open models and make them better on a customer's task: SFT, LoRA and other parameter-efficient methods, reinforcement learning (RLHF, RLVR), distillation. Just as important, you design evaluations that reflect the real task and let them drive training decisions, and you know when fine-tuning is not the answer. 3. Infrastructure and performance. You know your GPUs: memory bandwidth, kernels, parallelism, what utilization numbers actually mean. Serving and inference optimization: quantization, speculative decoding, batching, KV cache behavior, latency vs throughput tradeoffs, multi-GPU and multi-node serving, capacity planning. You can take a deployment that works and make it fast and economical, and reason about where the bottleneck is before touching a profiler. 4. Customer engagement. You want to be in the room. The point of this role is creating specialized intelligence for each customer: the best model for their task, their data, their constraints. That takes real interest in the customer's problem, not just the technical one. Exceptional here looks like founder energy with customers; the floor is genuine appetite to embed, explain, and own outcomes with them. KEY RESPONSIBILITIES: - Own Engagements: Take customer deployments from scoping through production, as the single accountable technical owner. - Embed: Work directly inside customer teams; understand their domain, data, and constraints well enough to make decisions they trust. - Train and Tune: Run fine-tuning and post-training work where the engagement needs it, from data to evaluation to a model serving in production. - Optimize: Drive serving performance and cost on Fireworks: model choice, deployment shape, and inference optimization. - Scope Honestly: Assess what is achievable before we commit, and inherit what you scope. - Product Signal: Feed what customers need back to product and engineering, with enough precision to be actionable. MINIMUM QUALIFICATIONS: - Spikes in two or all three areas. - For the ML spike: formal training (PhD, PhD in progress, or a strong MSc) and experience post-training LLMs specifically. - For the infrastructure spike: experience with GPU infrastructure, distributed serving, or inference engines. - Founder or founding engineer experience. - Experience in a startup or fast-paced environment. WHY FIREWORKS? - Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. - Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. - Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. - Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.

👤 HumanFull-timeMid

By Fireworks AISep 2, 2026

AI Field Engineer - Strategic Partnerships

Negotiable

ABOUT US: Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows, and products. Founded by the team behind PyTorch and backed by AMD, Atreides, Benchmark Capital, Index Ventures, Lightspeed, NVIDIA, Sequoia Capital, and TCV, Fireworks powers production AI with hundreds of state-of-the-art open models across text, image, embedding, audio, and multimodal workloads. Today, Fireworks is a Series D company valued at $17.5 billion, bringing together an ambitious, collaborative team that's building the future of enterprise AI. In the last few months alone we launched Fireworks Training, partnered with Microsoft Azure Foundry, and published research straight from our production systems. A few examples of what that looks like in practice: - Frontier RL is cheaper than the mega-cluster narrative suggests: we ran cross-region rollouts using 98% sparse weight deltas and published what we learned. (blog https://fireworks.ai/blog/frontier-rl-is-cheaper-than-you-think) - Open source agents with frontier advisors: matching frontier performance through training and harness engineering. (blog https://fireworks.ai/blog/open-source-agents-frontier-advisors) - The fine-tuning bottleneck is not the algorithm: integration friction and iteration speed are what actually stall teams; we documented the patterns across dozens of customer engagements. (blog) https://fireworks.ai/blog/fine-tuning-bottlenecks The Role As an AI Field Engineer for Strategic Partnerships, you will be one of the technical owners of Fireworks' most strategic partnership. You’ll work closely with Strategic Partner field teams, Partner-aligned ISVs, and the SIs that run enterprise AI transformation programs to make Fireworks the default inference and fine-tuning layer in every Partner AI architecture. The role sits at the intersection of engineering, partner development, and customer delivery. You build reference architectures, run benchmarks, debug production integrations, and co-develop POCs — all while holding your own in executive-level conversations about strategy, roadmap, and business outcomes. You spend most of your time building and enabling. You ship code, run joint POCs with Partner field teams, and architect deployments that span Strategic Partners and Fireworks. But you also lead discovery conversations, align partner stakeholders, and translate field signals into product improvements that compress the feedback loop from partner to roadmap. The Segment As a Field Engineer aligned with our Partnerships team you own the technical relationship between Fireworks and the Partner ecosystems, Partner field teams, ISVs building on Strategic Partners, and the SIs that deliver AI transformation programs on Strategic Partners. As an example, the Microsoft partnership is a core go-to-market bet: clients like UIPath, Stack Blitz, Motif run via Fireworks on Foundry.. Your job is to scale that pattern across the partner ecosystem. These engagements involve large, multi-stakeholder organizations, so you will need to navigate both the enterprise buyer (IT, security, compliance) and the builder (ML engineers, platform teams, app developers), while building the trusted-advisor relationships inside Microsoft's field that multiply your reach. What You'll Work On Technical Delivery and Deployment - Be the technical lead on co-sell motions with Strategic Partners — joint reference architectures, partner integration patterns, and shared POCs for strategic accounts. - Build end-to-end POCs and MVPs alongside partner engineering teams, working inside their codebases, infrastructure, and constraints. - Run load tests and establish latency, throughput, and cost baselines against realistic customer traffic profiles, and tune deployments to hit those targets. - Deploy and validate new model families on inference frameworks (vLLM, SGLang), determining optimal shapes, quantization configs, and serving patterns across workloads. Model Strategy and Fine-Tuning - Guide customers on model selection, fine-tuning strategy (SFT, DPO, RFT), and evaluation methodology. - Build and run fine-tuning pipelines directly with customers, navigating trade-offs between model families, compute cost, and quality targets. - Design and implement evaluation frameworks that measure production-quality metrics, not just benchmark scores Product Feedback and Platform Improvement - Own the feedback loop — surface partner-driven product gaps to Fireworks engineering, and translate the roadmap back into partner messaging. - Ship external technical content: reference architectures, integration guides, and benchmark posts that make it easy for partners to win deals with us. - Track pipeline health; flag risks and opportunities to Field leadership weekly What We're Looking For: Minimum Qualifications - 3+ years in a pre-sales, partner engineering, forward-deployed, or technical consulting role. - Demonstrated ability to build production software with customers, not just advise on it. You have shipped code running in someone else's production environment. - Strong Python skills. Comfortable reading, writing, and debugging production code. Familiarity with Kubernetes and infrastructure engineering. - Hands-on fluency with LLM inference: latency/throughput tradeoffs, batching strategies, quantization, structured outputs, function calling. You can explain why 50ms p99 matters to an enterprise CTO. - Real experience with fine-tuning — LoRA at minimum, RFT a strong plus. You understand when SFT is enough and when it isn't. - Deep familiarity with the Azure AI stack: Azure Foundry, Azure OpenAI Service, Azure ML, AKS, Entra/RBAC for AI workloads. You know where Fireworks fits and where it doesn't. - Exceptional communication: able to run a sharp discovery call, present to a VP, and debug a latency issue with an ML engineer in the same afternoon. Preferred Qualifications - 5+ years in technical field or engineering roles where you've owned a technical relationship with a hyperscaler or major SI, not just supported one - Experience with inference serving frameworks (vLLM, SGLang, TensorRT-LLM) and tuning deployments for real workloads. - Prior role at a hyperscaler, AI-native cloud, or inference provider. - Deep familiarity with other strategic partner stacks: Platforms for AI workloads, network, and identity integration patterns. You know where Fireworks fits and where it doesn't. - Experience with agentic frameworks (LangChain, LlamaIndex, or custom tool-use pipelines) — you understand how inference latency and reliability shapes agent behavior at scale. - Background in model evaluation — you understand why benchmark gaming is rampant and what rigorous evals actually look like. - You've written a technical blog post or reference architecture that people actually read. - Track record taking GenAI POCs from prototype to production-scale deployments. On-Target Expectations (Plus Equity) $200,000 - $260,000 USD Total compensation also includes meaningful equity in a fast-growing startup, along with a competitive salary and comprehensive benefits package. Base salary is determined by a range of factors including individual qualifications, experience, skills, interview performance, market data, and work location. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators. WHY FIREWORKS? - Solve Hard Problems: Tackle challenges at the forefront of AI infrastructure, from low-latency inference to scalable model serving. - Build What’s Next: Work with bleeding-edge technology that impacts how businesses and developers harness AI globally. - Ownership & Impact: Join a fast-growing, passionate team where your work directly shapes the future of AI—no bureaucracy, just results. - Learn from the Best: Collaborate with world-class engineers and AI researchers who thrive on curiosity and innovation. Fireworks AI is an equal-opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all innovators.

👤 HumanFull-timeSenior

By Fireworks AIJun 11, 2026

Senior Research Engineer, Audio and Speech

Negotiable

About Decagon Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences. Our technology enables industry-defining enterprises like Avis Budget Group, Block’s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel. We’re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We’re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others. We’re an in-office company, driven by a shared commitment to excellence and velocity. Our values — Just Get It Done, Invent What Customers Want, Winner’s Mindset, and The Polymath Principle — shape how we work and grow as a team. About the Team Read more about the Speech Research Team's work: - https://decagon.ai/blog/audio-native-semantic-speaker-change-detection - https://decagon.ai/blog/scaling-real-time-tts-inference - https://decagon.ai/blog/teaching-flow-matching-tts-with-rl The Research team develops the model and decision-making stack that powers Decagon’s conversational agents for enterprise support. We research, adapt, and implement state-of-the-art techniques in model training, prompting, orchestration, and evaluation in order to make our agents more accurate, robust, and efficient in real-world deployments. Our goal is to push the frontier of applied conversational AI: agents that reliably understand nuanced intent, track long context, and take the right actions under uncertainty. We measure success the way customers feel it: higher resolution rates, better user satisfaction, and consistent behavior at scale. About the Role As a Research Engineer focused on Audio and Speech, you’ll be responsible for building the models and agent harnesses that power Decagon’s real-time voice agents and taking them all the way from idea to production. Your work will advance multimodal and full-duplex systems that can listen, reason, speak, and respond naturally in real time. We’re looking for strong engineers who want to build the next generation of AI voice agents. People here own their work end-to-end, ship real improvements, and are trusted to make high-impact technical decisions. In this role, you will - Design and build next-generation agent harnesses optimized for streaming speech, turn-taking, interruptions, overlapping speech, and continuous interaction - Research and train multimodal and full-duplex models that jointly understand audio, reason, and generate speech - Improve speech recognition, voice activity detection, endpointing, and speech generation across diverse speakers, environments, domains, and languages - Build evaluations and use production calls to ship measurable improvements in accuracy, latency, naturalness, and task outcomes - Optimize end-to-end inference for responsiveness, throughput, stability, and cost, partnering with Voice Platform and Infrastructure teams to deploy at scale Your background looks something like this - 4+ years of experience in speech, audio ML, multimodal ML, or production machine learning - Experience developing or adapting autoregressive, diffusion, flow-matching, or codec-based speech models - Hands-on experience with streaming agent systems, low-latency inference, production model serving, and evaluation on real-world audio - Fluency in Python and a modern deep-learning framework such as PyTorch, with strong foundations in machine learning and signal processing - A track record of taking research ideas from prototype to reliable, measurable production impact Even better if you have - Familiarity with speech-to-speech or full-duplex models - Experience with telephony, multilingual speech, noisy-channel robustness, speaker adaptation, or expressive speech generation Compensation $200K – $400K + Offers Equity Benefits We proudly offer the following benefits for our full-time employees: - Medical, Dental, and Vision benefits for you and your family - Life Insurance and Disability Benefits - Retirement Plan (e.g., 401K, pension) - Parental Leave - Fertility and family building benefits through Carrot - Monthly stipend to support your wellness, lifestyle, and work-life balance - Daily lunches and snacks in the office to keep you at your best - Take what you need vacation policy (subject to local requirements; UK employees receive 25 days of statutory leave) These benefits are described in more detail in Decagon’s policies, may vary by location, and can change at any time according to applicable compensation and benefits plans.

👤 HumanFull-timeSenior

By DecagonMay 18, 2026

Research Engineer, Audio and Speech

Negotiable

Negotiable

🚀 ABOUT WRITER WRITER is where the world's leading enterprises orchestrate AI-powered work. Our vision is to expand human capacity through superintelligence. And we're proving it's possible – through powerful, trustworthy AI that unites IT and business teams together to unlock enterprise-wide transformation. With WRITER's end-to-end platform, hundreds of companies like Mars, Marriott, Uber, and Vanguard are building and deploying AI agents that are grounded in their company's data and fueled by WRITER's enterprise-grade LLMs. Valued at $1.9B and backed by industry-leading investors including Premji Invest, Radical Ventures, and ICONIQ Growth, WRITER is rapidly cementing its position as the leader in enterprise generative AI. Founded in 2020 with office hubs in San Francisco, New York City, Austin, Chicago, and London, our team thinks big and moves fast, and we're looking for smart, hardworking builders and scalers to join us on our journey to create a better future of work with AI. 📐 ABOUT THE ROLE AI research at WRITER isn't just about publishing papers — it's about building the scientific foundation that powers some of the most ambitious enterprise AI deployments in the world. As an AI research scientist, you'll be at the center of that work. You'll drive a high-impact research agenda focused on large language models, agentic reasoning, and the system-level capabilities that make AI genuinely useful at enterprise scale. This is a rare opportunity to do research that matters twice over — advancing the field and shipping directly into products used by hundreds of thousands of people every day. We're at an inflection point. Enterprises are moving from experimenting with AI to deeply embedding it across their operations, and WRITER's models are the engine making that possible. The work you do here — on post-training, planning, multi-step reasoning, and agentic workflows — will directly shape how the next generation of enterprise AI behaves, performs, and scales. You'll have the resources, infrastructure, and cross-functional support to pursue ambitious ideas and bring them to life quickly. This role is hybrid, based out of our San Francisco or New York City hub. You'll report to our VP of AI research. Please note: role is open to Senior, Staff, and Sr. Staff applicants We are open to hiring for this role at multiple levels, with compensation ranges scaled to match your experience, expertise, and the scope of the role. 🦸🏻♀️ WHAT YOU'LL DO - Lead an independent, high-impact research agenda on large language models and agentic systems, owning projects from early hypothesis through model training, evaluation, and production deployment - Design and execute large-scale post-training experiments using supervised fine-tuning, reinforcement learning from human feedback (RLHF), RLAIF, DPO, and emerging alignment techniques — with a focus on improving multi-step reasoning, planning, and tool use in enterprise agentic workflows - Build novel evaluation benchmarks and methodologies that push beyond existing limitations, establishing rigorous measures for how well models perform on complex, real-world enterprise tasks - Develop scalable data synthesis and curation pipelines that generate the high-quality training signal driving model improvement — including LLM-as-judge frameworks, synthetic data generation, and adversarial dataset construction - Shape WRITER's model architecture and training roadmap by translating your research insights into concrete improvements to our enterprise-grade LLMs, working hand-in-hand with research engineering and product teams - Publish and present original research at top-tier venues — NeurIPS, ICLR, ICML, ACL, and others — representing WRITER at the frontier of the field and contributing to the broader scientific community - Mentor and uplevel fellow researchers and engineers on the team, helping set a high bar for scientific rigor, experimental design, and research culture ⭐️ WHAT YOU NEED - 7+ years of hands-on ML research experience, with deep expertise in large language model pre-training and post-training — you've trained models at scale, debugged distributed jobs, and shipped improvements that made a measurable difference - A Ph.D. in Computer Science, Machine Learning, NLP, or a related field — or equivalent demonstrated research experience with a strong portfolio of independent, published work - Expert-level knowledge of post-training methods including SFT, RLHF, RLAIF, DPO, GRPO, and related alignment and reasoning techniques, with a track record of applying them to real, production-grade systems - Strong command of Python and PyTorch (or JAX), with the engineering depth to build and scale training pipelines, evaluation infrastructure, and data synthesis workflows yourself — not just direct others to do it - A meaningful publication record at competitive ML/AI venues (NeurIPS, ICLR, ICML, ACL, EMNLP, or equivalent), evidencing your ability to originate ideas and execute on a multi-month research agenda independently - Hands-on experience designing or evaluating agentic systems — models that plan, reason through multi-step tasks, use tools, and recover gracefully from errors — with a nuanced understanding of where they break and how to fix them - The instincts and orientation that match WRITER's values: you Connect — you collaborate openly across research, engineering, and product and communicate complex ideas with clarity to both technical and non-technical audiences; you Challenge — you ask the hard questions, push back on conventional wisdom, and pursue the research directions others haven't tried yet; you Own — you drive your projects end-to-end with urgency, take accountability for results, and care deeply about the impact your work has on real customers 🍩 Benefits & perks (US Full-time employees) - Generous PTO, plus company holidays - Medical, dental, and vision coverage for you and your family - Paid parental leave for all parents (16 weeks) - Fertility and family planning support - Early-detection cancer testing through Galleri https://www.galleri.com/partner/writer - Flexible spending account and dependent FSA options - Health savings account for eligible plans with company contribution - Annual work-life stipends for: - Wellness stipend for gym, massage/chiropractor, personal training, etc. - Learning and development stipend - Company-wide off-sites and team off-sites - Competitive compensation, company stock options and 401k WRITER is an equal-opportunity employer and is committed to diversity. We don't make hiring or employment decisions based on race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other basis protected by applicable local, state or federal law. Under the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. By submitting your application on the application page, you acknowledge and agree to WRITER's Global Candidate Privacy Notice https://writer.com/legal/candidate-privacy-notice/.

👤 HumanFull-timeSenior

By WriterSep 22, 2026

Product Manager

Negotiable

About the Role We are looking for an experienced AI Product Manager who is passionate about building intelligent products customers love. You will join a dynamic, fast-paced environment and work with cross-functional teams — engineering, data science, design, and go-to-market — to design, build, and launch AI-powered products that deliver our company's vision and strategy. This role sits at the intersection of applied AI/ML, customer outcomes, and business strategy: you'll define what we build, why we build it, and how we measure success in a rapidly evolving AI landscape. What You Will Be Doing - Own the AI product roadmap — including LLM-powered features, predictive models, agentic workflows, and ML-driven automation — translating customer needs into a prioritized, outcome-driven plan. - Develop a deep understanding of customer experience, identify product gaps where AI can deliver disproportionate value, and generate new ideas that grow market share, improve customer experience, and drive growth. - Partner closely with data scientists, ML engineers, and software engineers to scope models, evaluate tradeoffs between accuracy, latency, cost, and UX, and ship with quick time-to-market and optimal resources. - Translate product strategy into detailed requirements and prototypes, including user stories, MVP scope, acceptance criteria, model evaluation criteria, and KPI frameworks tied to measurable customer outcomes. - Define and monitor AI feature success metrics — including model performance (precision, recall, accuracy), user adoption, and business impact — through dashboards and regular reporting to executive leadership. - Conduct structured buy / build / partner analysis for new AI capabilities, including evaluation of foundation model providers, third-party AI vendors, and open-source alternatives. - Scope and prioritize activities based on business and customer impact, balancing near-term wins with longer-horizon AI investments. - Drive product launches in collaboration with marketing, PR, executives, and other PM team members — including positioning AI capabilities clearly and avoiding overpromising. - Evaluate promotional plans to ensure messaging accurately conveys AI capabilities and limitations, and is consistent with the broader product line strategy. - Act as an AI product evangelist — both internally and externally — to build awareness, understanding, and trust in our AI capabilities. - Represent the company by visiting customers to solicit feedback on AI features, observe real-world use, and surface opportunities for the next wave of intelligent product capabilities. Your Qualifications Required - BS/MS degree in Computer Science, Engineering, Data Science, or equivalent practical experience. - 3–5 years of proven product management experience, with at least 1–2 years owning AI/ML-powered product features in a SaaS environment. - Proven track record of managing all aspects of a successful product throughout its lifecycle — from discovery through launch and iteration. - Demonstrated ability to develop product and AI strategies and effectively communicate recommendations to executive leadership. - Solid technical background with hands-on understanding of software development, modern AI/ML technologies (LLMs, embeddings, classical ML), and SaaS architectures. - Ability to read and interpret ML model outputs, understand evaluation metrics (precision/recall, AUROC, RMSE, F1, eval sets for LLMs), and hold technical teams accountable to model quality. - Strong written, verbal, presentation, and communication skills — especially the ability to explain AI concepts to non-technical audiences. - A passion for usability, user empathy, and a knack for simplicity — particularly in designing AI experiences that build user trust. - Strong problem-solving skills and willingness to roll up your sleeves to get the job done. - Skilled at working effectively with cross-functional teams in a matrix organization. Preferred - Hands-on experience shipping LLM-based product features, RAG architectures, fine-tuned models, or agentic AI applications. - Familiarity with AI governance frameworks, model explainability, evaluation harnesses, and responsible AI practices. - Experience defining prompt engineering standards, eval pipelines, or human-in-the-loop workflows for AI products. - Exposure to MLOps tooling and lifecycle (model training, deployment, monitoring, drift detection). Travel - Travel expectation of approximately 10–15% to meet with customers, partners, and team members. Why Join Us - Shape the AI strategy and roadmap of a fast-growing company at a defining moment in the AI industry. - High-visibility role with direct exposure to executive leadership and top customers. - Work with a talented, cross-functional team of engineers, data scientists, and designers building the future of intelligent software. - $130,000 to $150,000 base salary + annual bonus opportunity We use artificial intelligence (AI) tools to assist in certain stages of our recruitment process, such as resume screening and candidate matching. These tools are designed to support fair and consistent evaluations. If you have questions about this process or would like to request an alternative assessment method, please contact us at hr@cleo.com. Cleo Communications US, LLC is an equal opportunity/affirmative action employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability status, protected veteran status, or any other characteristic protected by law.

👤 HumanFull-timeRemoteSenior

By CleoFeb 24, 2026

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