[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) # Senior Customer AI Engineer – 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[Technical Support](https://jobicy.com/categories/technical-support.md)EmploymentFull TimeExperienceSeniorPublished23 Oct 2026Apply before22 Nov 2026Listing views31Application actions2Application 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 hiring a senior customer-facing AI engineer to guide enterprise workloads from proof of concept into stable, scalable production on its AI cloud platform. The role combines production delivery, technical customer success, performance troubleshooting, and incident coordination rather than sales, greenfield solution architecture, or reactive ticket support. The engineer will optimize latency, throughput, reliability, and cost efficiency for AI inference workloads while partnering with customer engineering teams and internal product and infrastructure groups. Strong practical knowledge of cloud or distributed systems and inference frameworks such as vLLM or TensorRT is central to success. The position is remote within Europe and requires a proactive owner who can manage several customer priorities in a fast-moving environment. ## 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 production-facing role involving high-load AI infrastructure, inference performance, and customer incidents where technical errors can directly affect workload reliability and cost. It also requires independent prioritization across multiple customers and clear coordination under pressure. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Market rate$165,000US market range (per year)$135k–$200k0$220.00k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. The yearly USD figures are AI estimates for a senior customer-facing AI infrastructure engineer and are not verified employer compensation. ## Core skills Skills and capabilities most closely associated with this opportunity. [AI inference](https://jobicy.com/jobs?search_keywords=AI%20inference.md)[vLLM](https://jobicy.com/jobs?search_keywords=vLLM.md)[TensorRT](https://jobicy.com/jobs?search_keywords=TensorRT.md)[GPU workloads](https://jobicy.com/jobs?search_keywords=GPU%20workloads.md)[Cloud infrastructure](https://jobicy.com/jobs?search_keywords=Cloud%20infrastructure.md)[Distributed systems](https://jobicy.com/jobs?search_keywords=Distributed%20systems.md)[Performance optimization](https://jobicy.com/jobs?search_keywords=Performance%20optimization.md)[Latency optimization](https://jobicy.com/jobs?search_keywords=Latency%20optimization.md)[Reliability engineering](https://jobicy.com/jobs?search_keywords=Reliability%20engineering.md)[Incident management](https://jobicy.com/jobs?search_keywords=Incident%20management.md)[Customer engineering](https://jobicy.com/jobs?search_keywords=Customer%20engineering.md)[Technical troubleshooting](https://jobicy.com/jobs?search_keywords=Technical%20troubleshooting.md)[Cost optimization](https://jobicy.com/jobs?search_keywords=Cost%20optimization.md)[Production deployment](https://jobicy.com/jobs?search_keywords=Production%20deployment.md)[Stakeholder communication](https://jobicy.com/jobs?search_keywords=Stakeholder%20communication.md) Sample interview questionsHow would you take a customer's LLM workload from proof of concept to a reliable production deployment?I would first establish the workload's functional requirements, traffic profile, latency and throughput targets, reliability objectives, and cost constraints. I would validate the deployment architecture, observability, capacity plan, failure handling, and rollback procedures, then run staged load and resilience tests. After launch, I would monitor agreed service indicators and use a regular review cadence to prioritize optimizations. A customer reports rising inference latency after traffic growth. How would you investigate? I would begin by correlating latency with request rate, queue depth, GPU utilization, batch size, token lengths, model-server metrics, network behavior, and recent deployment changes. I would isolate whether the constraint is request queuing, model execution, memory pressure, infrastructure capacity, or a downstream dependency. I would communicate findings and mitigation steps clearly, such as tuning batching, scaling capacity, adjusting routing, or optimizing the serving configuration. How do you balance performance, reliability, and cost for an AI inference deployment? I would define measurable targets for all three dimensions with the customer, because optimizing one in isolation can create unacceptable trade-offs. I would use workload measurements to select appropriate hardware, autoscaling thresholds, batching settings, model variants, and redundancy patterns. Recommendations would be validated with benchmarks and expressed in business terms, including expected impact on latency, availability, and unit cost. Describe how you would manage a production incident involving a strategic customer. I would establish ownership, scope the customer impact, and create a concise incident channel with clear roles and update intervals. I would prioritize mitigation and safe restoration of service while collecting evidence for root-cause analysis and coordinating the necessary infrastructure, product, and customer teams. After resolution, I would provide a transparent summary, corrective actions, owners, and follow-up dates to reduce recurrence. How do you communicate a complex infrastructure limitation to a technical customer? I would explain the limitation through the customer's observed symptoms and desired outcome, then share the relevant technical evidence without unnecessary jargon. I would clearly separate confirmed facts, assumptions, risks, and available options, including trade-offs. I would finish with a recommended path, concrete next steps, and success metrics so the customer can make an informed decision. 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 We’re looking for a Senior Customer AI Engineer who will join our Token Factory team to help our customers successfully transition from proof-of-concept to production and scale their AI workloads on Nebius infrastructure. This role sits at the intersection of engineering, delivery, and customer success – ensuring that what was promised during pre-sales actually works reliably in production. You will work closely with customer engineering teams, Solution Architects, and Product/Infrastructure teams to drive stable, performant, and cost-efficient deployments. This role is NOT: * A sales role (though you will support expansion through value) * A pure support role (you won’t just react to tickets) * A solution architect role (you won’t design systems from scratch) You’re welcome to work remotely from Europe. Your responsibilities will include: Own the production journey * Lead the transition from PoC to production * Ensure customer workloads are deployed, stable, and scalable * Drive time-to-production and time-to-value Ensure technical success in production * Understand customer architectures and use cases * Monitor and improve: – performance (latency, throughput) – cost efficiency – reliability * Identify and resolve bottlenecks proactively Act as a trusted technical partner * Work directly with customer engineering teams * Provide guidance on best practices and optimisation * Translate technical challenges into actionable solutions Manage risks and incidents * Act as a primary technical contact for production issues * Coordinate with internal teams to resolve incidents * Communicate clearly during high-pressure situations Drive continuous improvement * Identify opportunities to optimise and expand usage * Provide structured feedback to Product and Infrastructure teams * Help shape better solutions based on real customer needs We expect you to have: Technical background * Practical knowledge of inference frameworks (e.g. vLLM, TensorRT, or similar) * Solid understanding of: – cloud or infrastructure systems – distributed systems or high-load applications – AI/ML workloads (LLMs, inference, etc.) is a strong plus * Ability to troubleshoot and reason about system performance Customer-facing experience * Experience working directly with technical customers (e.g. engineers, ML teams) * Ability to communicate complex topics clearly and effectively Ownership & execution * Strong sense of ownership – you drive outcomes, not just tasks * Ability to manage multiple customers and priorities * Structured, proactive, and solution-oriented mindset It will be an added bonus if you have: * Experience with GPU workloads or AI infrastructure * Background in solutions engineering, SRE, or technical support in B2B environments 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/156535-senior-customer-ai-engineer-token-factory.md) > Annual salary information is not provided for this position. 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