Manager, ML Solutions Architecture - Token Factory

This position sits within Nebius Token Factory, our serverless platform for running and customizing open-source LLMs in production. Token Factory allows for serverless inference and fine-tuning backed by in-house optimizations like custom speculative decoding, quantization, cache-aware routing and dedicated endpoints. Customers come to us to move from prototype to scaled production without the cost and complexity of building and tuning their own inference stack.

Our Solutions Architects own the technical delivery of customer engagements: deploying open-source models, tuning the serving stack, benchmarking against the customer's success criteria, and carrying the technical relationship through to production. Sales Engineers qualify and scope the opportunity; SAs execute it. Technical Account Managers take it from production onward.

We're looking for a Manager, ML Solutions Architecture to lead our EU regional SA teams. Your scope is PoC delivery and post-sales technical support: the people who do it, the standard they do it to, and the operating cadence that keeps it predictable. You will report to the Head of Solutions Architecture and partner with a peer manager in the other region.

We expect you to push people when they need pushing, make the uncomfortable call when deliverables don't land even though the effort was real, and take ownership of the documentation work: implement and monitor the following of the guidelines, ticket hygiene, and making sure the team actually uses all three.

You're welcome to work remotely from any EU country.

• Programming languages: Python
• ML frameworks and libraries: vLLM, TensorRT-LLM, SGLang, Transformers, OpenAI/Anthropic SDKs
• MLOps and DevOps tools: Kubernetes (K8s), Docker, Git
• Cloud platforms: AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure (Azure ML)

• Manage a team of 8 Solutions Architects, with continued growth planned: 1:1s, goal setting, performance reviews, promotion cases, and individual growth plans
• Build an accurate picture of each SA's strengths, gaps, and preferences, and allocate accounts and engagements against both expertise and interest
• Run the cadence that surfaces blockers early, then take them to the development, product, and business teams that can clear them, and stay on them until they do
• Hold people to outcomes: distinguish effort from delivered results, say so plainly when the two diverge, and reflect it in ratings and compensation decisions