[All remote jobs](https://jobicy.com/jobs.md)[![Nebius logo](https://jobicy.com/data/server-nyc0409/galaxy/mercury/2026/06/d90c0566-221.webp)](https://jobicy.com/company/nebius.md)[Nebius](https://jobicy.com/company/nebius.md) # Manager, ML Solutions Architecture – Token Factory Review the role, location requirements, compensation details, and application process before deciding whether this opportunity fits your next career move. [Apply for this job](#job-application)[View company](https://jobicy.com/company/nebius.md)ShareRemote from[Europe](https://jobicy.com/job-region/europe.md)SalaryUndisclosedDepartment[Product & Operations](https://jobicy.com/categories/management.md)EmploymentFull TimeExperienceSeniorPublished20 Oct 2026Apply before19 Nov 2026Listing views35Application actions1Application toolkit ## Make your next move. Prepare your resume, explore your fit, and draft a cover letter for this opportunity. AI Summary ## The role, at a glance. Nebius is seeking a manager to lead an EU-based ML Solutions Architecture team delivering customer proofs of concept, production inference deployments, and post-sales technical support. The role combines people management for a growing team of Solutions Architects with accountability for delivery quality, staffing, benchmarks, documentation, and operating metrics. The manager must have strong hands-on judgment in LLM inference, fine-tuning, evaluation methodology, and frameworks such as vLLM, SGLang, and TensorRT-LLM. Success requires managing customer-facing escalations, challenging infeasible requirements early, and translating recurring customer needs into platform priorities across engineering, product, and business teams. ## Role DNA A quick view of the complexity, pace, ownership and collaboration implied by the job description. ### Job Complexity 5/5EasyHard ### Pace & Pressure 5/5RelaxedFast-paced ### Autonomy Level 5/5GuidedFull ownership ### Communication Load 5/5IndependentCollaborative AI insightThis is a senior technical leadership role requiring both deep LLM-serving expertise and direct accountability for customer delivery, team performance, and operational rigor. The manager must make difficult prioritization and performance decisions in a fast-moving, distributed environment. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$190,000US market range (per year)$160k–$230k0$253.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary is disclosed. The yearly USD figures are AI estimates for a senior manager of customer-facing ML solutions architecture with LLM inference expertise; the yearly cadence is inferred from the full-time senior leadership context. ## Core skills Skills and capabilities most closely associated with this opportunity. [People management](https://jobicy.com/jobs?search_keywords=People%20management.md)[Solutions architecture](https://jobicy.com/jobs?search_keywords=Solutions%20architecture.md)[LLM inference](https://jobicy.com/jobs?search_keywords=LLM%20inference.md)[Model fine-tuning](https://jobicy.com/jobs?search_keywords=Model%20fine-tuning.md)[ML evaluation design](https://jobicy.com/jobs?search_keywords=ML%20evaluation%20design.md)[vLLM](https://jobicy.com/jobs?search_keywords=vLLM.md)[TensorRT-LLM](https://jobicy.com/jobs?search_keywords=TensorRT-LLM.md)[SGLang](https://jobicy.com/jobs?search_keywords=SGLang.md)[Python](https://jobicy.com/jobs?search_keywords=Python.md)[Customer success](https://jobicy.com/jobs?search_keywords=Customer%20success.md)[Technical account management](https://jobicy.com/jobs?search_keywords=Technical%20account%20management.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[Docker](https://jobicy.com/jobs?search_keywords=Docker.md)[Cloud infrastructure](https://jobicy.com/jobs?search_keywords=Cloud%20infrastructure.md)[Operational process design](https://jobicy.com/jobs?search_keywords=Operational%20process%20design.md) Sample interview questionsHow would you evaluate whether a customer proof of concept is ready to move to a dedicated production endpoint?I would first align the decision to explicit success criteria, including quality, latency, throughput, reliability, security, and operational ownership. I would review the benchmark methodology and raw results for representative workloads, validate the serving configuration, document known limitations, and confirm a clear handoff plan for production support. Describe how you would identify a flaw in an LLM inference benchmark prepared by a Solutions Architect. I would inspect whether the workload reflects real request patterns, whether warm-up and caching effects were controlled, and whether metrics such as time-to-first-token, inter-token latency, throughput, and error rate were measured consistently. I would also verify model, quantization, hardware, concurrency, batching, and routing assumptions before accepting the conclusion. How would you handle a customer requirement that the current platform cannot feasibly meet? I would communicate the constraint early with evidence, including benchmark data, architectural limits, and the expected impact of alternatives. I would work with the account and product teams to define a viable scope, escalation path, or roadmap request rather than allowing the team to spend unbounded iterations on an infeasible target. What operating cadence would you establish for a distributed Solutions Architecture team? I would use regular one-to-ones, delivery reviews, account-health and capacity reviews, and structured escalation checkpoints. The ticket tracker would remain the system of record, supported by clear definitions of done, engagement templates, ownership rules, and metrics such as time to optimized endpoint and success-criteria hit rate. How do you balance people development with accountability when an engineer is putting in effort but repeatedly missing outcomes? I would make the gap between effort and outcomes explicit, identify whether the cause is skills, scope, prioritization, or support, and agree on concrete measurable improvement actions. I would provide coaching and appropriate support, but consistently reflect sustained delivery performance in feedback, ratings, and growth decisions. Opportunity details ## About this role. 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. ### 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 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. Your responsibilities will include: Lead the team * 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 * 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) 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. Show more [Apply now >](https://jobicy.com/jobs/155956-manager-ml-solutions-architecture-token-factory.md) > Annual salary information is not provided for this position. 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