Job description

About Nebius: Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D. Based in the San Francisco Bay Area, or willing to relocate. The role We're looking for a scrappy, resourceful Developer Advocate who understands inference to help developers run open models in production on Token Factory, Nebius' high-performance inference platform. Every agent, copilot and AI product runs on an inference layer, and the developers choosing that layer have hard questions: Which open model should I run for this step? What does this cost per million tokens at my traffic? Why is my p99 latency spiking? Why does my multi-turn agent get slower and more expensive as the context grows? When do I move from a serverless endpoint to a dedicated one, and when does fine-tuning beat a longer prompt? Your job is to have credible, hands-on answers to those questions, and to show the work in public. You'll be embedded with the Token Factory product and engineering team in San Francisco. You'll be in their standups and bug bashes, you'll carry customer stories back to them, and you'll fix the docs gap yourself when you find one. You'll be equally at home in the Bay Area's AI-native community: the meetups our customers and partners host, and the open-source inference projects developers actually use. This is a hands-on, builder-first role. The content that matters here is less "how to call the API" and more "here's how a production inference stack is put together, and here are the tradeoffs." If you have strong opinions about serving open models and want a platform to prove them on, this role is for you. This role is based in the San Francisco Bay Area. The Token Factory team's center of gravity is in San Francisco and we expect this person to be part of the local community in person. Your responsibilities will include: Help developers and teams discover, evaluate and adopt Token Factory for production inference workloads, from a first API call on a serverless endpoint to dedicated endpoints and fine-tuned models. Build demos, benchmarks and reference architectures that make real tradeoffs concrete: which open model to use for which step, latency against cost per token, how context growth affects multi-turn agents, reliability of tool calling and structured outputs, and when fine-tuning or a custom speculator pays off versus a bigger model or a longer prompt. Show how Token Factory fits into the stack developers already use: OpenAI-compatible SDKs, agent frameworks, MCP and tool use, evals, embeddings and retrieval, and post-training. Be embedded with Token Factory product and engineering: join standups and bug bashes, test new models and features before launch, and make sure the right samples and docs exist on day zero of every release. Own the developer feedback loop. Gather what's breaking for builders, bring it back as concrete product input, and follow through until it ships or is explicitly declined. Represent Nebius in the Bay Area inference and open-model community: meetups, partner and customer events, open-source communities such as vLLM, SGLang and Ray, and developer conferences. Publish technical content and give talks that take developers from first hearing about Token Factory to running something on it, and partner with marketing to turn real builder stories into case studies. Work closely with the DevRel team and the Token Factory product marketing team on launches, community programs and content strategy. We expect you to have: Hands-on experience running open models in production or at scale through an inference provider or a serving stack, with a clear point of view on how you chose it and what broke. If you haven't tried Token Factory yet, we'll expect you to have done so before we talk. A working understanding of what happens under the hood of a serving engine like vLLM or SGLang: continuous batching, KV and prefix caching, quantization, speculative decoding, and how each shows up in latency and cost. You won't be tuning these on Token Factory, but you need to hold your own with the engineers who do. Current, first-hand knowledge of the open model ecosystem, including which models are worth recommending for which workloads and why. Comfort writing and reviewing code, reproducing issues, and shipping fixes to docs and samples yourself. A track record of building and explaining in public: talks, open-source contributions, writing, or an active technical presence on GitHub, X or LinkedIn. A DevRel title is not required. The ability to explain complex systems clearly to technical and non-technical audiences, and to be a credible technical partner to engineers, product managers and customers. Based in the San Francisco Bay Area, or willing to relocate. Candidates who come from forward-deployed engineering, solutions architecture, ML engineering or technical product roles are strongly encouraged to apply. It's a plus if you have: Hands-on experience with supervised fine-tuning, distillation or RL post-training of open models, and a view on when it beats a longer prompt or a bigger model. Contributions to open-source inference or distributed-compute projects such as vLLM, SGLang or Ray. Experience at an inference or GPU cloud provider, or as a heavy user of several. An existing audience that trusts your takes on models and infrastructure. #LI-LT1 Key employee benefits in the US: Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families. 401(k) plan: Up to 4% company match with immediate vesting. Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers. Remote work reimbursement: Up to $85/month for mobile and internet. Disability & life insurance : Company-paid short-term, long-term and life insurance coverage. Pay Transparency We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law. Base Compensation Range $179,500 — $224,300 USD Benefits & Perks: Competitive compensation Career growth and learning opportunities Flexibility and ownership Collaborative and innovative culture Opportunity to work on impactful AI projects International environment and talented teams What's it like to work at Nebius: Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI Equal Opportunity Statement: Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire. If you need accommodations during the application process, please let us know.

Requirements

Must-haves

We're looking for a scrappy, resourceful Developer Advocate who understands inference to help developers run open models in production on Token Factory, Nebius' high-performance inference platform.
Help developers and teams discover, evaluate and adopt Token Factory for production inference workloads, from a first API call on a serverless endpoint to dedicated endpoints and fine-tuned models.
Show how Token Factory fits into the stack developers already use: OpenAI-compatible SDKs, agent frameworks, MCP and tool use, evals, embeddings and retrieval, and post-training.
Be embedded with Token Factory product and engineering: join standups and bug bashes, test new models and features before launch, and make sure the right samples and docs exist on day zero of every release.
Own the developer feedback loop. Gather what's breaking for builders, bring it back as concrete product input, and follow through until it ships or is explicitly declined.
Represent Nebius in the Bay Area inference and open-model community: meetups, partner and customer events, open-source communities such as vLLM, SGLang and Ray, and developer conferences.
Publish technical content and give talks that take developers from first hearing about Token Factory to running something on it, and partner with marketing to turn real builder stories into case studies.
Work closely with the DevRel team and the Token Factory product marketing team on launches, community programs and content strategy.
Current, first-hand knowledge of the open model ecosystem, including which models are worth recommending for which workloads and why.
Comfort writing and reviewing code, reproducing issues, and shipping fixes to docs and samples yourself.
A track record of building and explaining in public: talks, open-source contributions, writing, or an active technical presence on GitHub, X or LinkedIn. A DevRel title is not required.
The ability to explain complex systems clearly to technical and non-technical audiences, and to be a credible technical partner to engineers, product managers and customers.
Based in the San Francisco Bay Area, or willing to relocate.
Candidates who come from forward-deployed engineering, solutions architecture, ML engineering or technical product roles are strongly encouraged to apply.
Hands-on experience with supervised fine-tuning, distillation or RL post-training of open models, and a view on when it beats a longer prompt or a bigger model.
Contributions to open-source inference or distributed-compute projects such as vLLM, SGLang or Ray.
Experience at an inference or GPU cloud provider, or as a heavy user of several.
An existing audience that trusts your takes on models and infrastructure. #LI-LT1
Remote work reimbursement: Up to $85/month for mobile and internet.
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI

Nice-to-haves

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Application prep

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Keywords to mirror

aimlapi

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Health Insurance

Dental

Vision

401(K)

Parental Leave