Manager, ML Solutions Architecture - Token Factory

About the Position

We are seeking a Manager, ML Solutions Architecture to lead our US regional SA teams in deploying and optimizing open-source LLMs at Nebius. This role is pivotal in ensuring the technical delivery of customer engagements, from deploying models to post-sales technical support.

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. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software, and AI R&D.

Responsibilities

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

Requirements

• 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

Nice to 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)

We Offer

• 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

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

$228,000-$285,000 USD

About the Company

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.