Manager, ML Solutions Architecture - Token Factory

The role

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 US 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 the United States.

Your responsibilities will include:

Lead the team

• Manage a team of 4 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
• Onboard new joiners through to their first independently delivered engagement
• Coach SAs into stronger engineers and stronger communicators, and make deliberate calls about who is ready for more scope

Own delivery

• Be accountable for your team's delivery outcomes: time from PoC kick-off to first optimized dedicated endpoint, success-criteria hit rate, and the quality of the technical relationship after the customer goes to production
• Review technical work before it reaches the customer: benchmarking methodology, serving configurations, results, closure documents; catch the wrong conclusion drawn from a metrics artifact before a customer sees it
• Ensure staffing and escalation coverage across accounts and timezones, including post-sales request load that does not respect sprint boundaries
• Call infeasibility early and with evidence, rather than letting the team burn iterations against requirements the platform cannot meet today

Own the operating system of the team

• Maintain and extend the team's documentation: responsibilities, runbooks, guides, onboarding, definitions of done, engagement closure templates. Keep it accurate and reachable; link, don't copy
• Get it used, not just written: documentation nobody reads is a cost, not an asset
• Keep the ticket tracker the system of record, so PoC and production status is readable without asking anyone
• Instrument the work: define and report the metrics that show whether delivery is getting faster and more reliable over time

Work across teams

• Development and research teams: convert recurring customer pain into prioritized platform work, and represent the customer's technical reality in roadmap discussions
• Pre-sales: hold the scoping-to-execution boundary: push back on under-scoped engagements, and feed feasibility signal back upstream
• Account management: make production handoffs uneventful, and keep post-sales technical requests moving
• Business and leadership: give a straight read on account health, technical feasibility, and capacity needs

We expect you to have:

• 3+ years managing technical teams, including performance management and difficult conversations
• Experience managing a customer-facing team. Running a team whose work is visible to customers, on customer timelines, with customer escalations.
• Strong ML knowledge: LLM architectures, fine-tuning approaches (SFT/LoRA, RL-based), evaluation design, and a working command of inference internals — quantization, KV-cache management, batching, routing, speculative decoding — and of the frameworks the team works in (vLLM, SGLang, TensorRT-LLM)
• Enough technical judgment to review someone else's benchmark and find the flaw in the methodology, not just in the conclusion
• Python strong enough to read and review your team's code
• Excellent communication skills, with the ability to clearly explain technical concepts to diverse audiences, from engineers to executives, including in front of enterprise customers under pressure
• Genuine tolerance for operational work: documentation, process design, reporting, and the follow-through that makes them stick
• Comfort operating with ambiguity across distributed teams and timezones, and a bias toward writing things down

It would be an added bonus if you have:

• References from both former managers and former direct reports
• Experience scaling a team through rapid growth (5 → 15+) without losing delivery quality
• Prior experience in a customer-facing technical function at a cloud, inference, or AI infrastructure provider
• Experience defining process and documentation for a team that had none
• Hands-on background running LLMs in production and debugging inference workloads at the framework level
• Work with multimodal AI models (vision-language, speech)
• Proficiency with DevOps tooling (Docker, Kubernetes) and infrastructure-as-code

Preferred technical stack:

• 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)