[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) # Forward Deployed Engineer, Physical AI Infrastructure 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[DevOps & Infrastructure](https://jobicy.com/categories/admin.md)EmploymentFull TimeExperienceOpen levelPublished20 Oct 2026Apply before19 Nov 2026Listing views62Application actions5Application 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 seeks a Forward Deployed Engineer to help Physical AI customers deploy, evaluate, and scale AI infrastructure workloads. The role combines hands-on infrastructure engineering with customer discovery, translating production requirements into input for cloud, serverless, and product teams. The engineer will build agents and prototypes, benchmark model-serving stacks, and troubleshoot systems spanning Python, Kubernetes, GPUs, networking, and storage. Success requires strong technical judgment, comfort with incomplete requirements, and the ability to communicate practical trade-offs to external engineers and internal product stakeholders. ## 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 highly technical customer-facing infrastructure role requiring breadth across AI systems, distributed compute, production operations, and LLM serving. The engineer must independently turn ambiguous customer problems into tested solutions and product recommendations in a fast-moving environment. ## Salary analysis Estimated compensation compared with the broader US market for similar roles. Estimated job median (per year)Highly competitive$205,000US market range (per year)$170k–$240k0$264k AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. Estimated annual compensation for a senior forward-deployed AI infrastructure engineer in the US market is approximately $170,000–$240,000 USD, with an estimated midpoint of $205,000; this is an AI estimate and not verified employer compensation. ## Core skills Skills and capabilities most closely associated with this opportunity. [Python](https://jobicy.com/jobs?search_keywords=Python.md)[Linux](https://jobicy.com/jobs?search_keywords=Linux.md)[Kubernetes](https://jobicy.com/jobs?search_keywords=Kubernetes.md)[Docker](https://jobicy.com/jobs?search_keywords=Docker.md)[GPU infrastructure](https://jobicy.com/jobs?search_keywords=GPU%20infrastructure.md)[LLM serving](https://jobicy.com/jobs?search_keywords=LLM%20serving.md)[vLLM](https://jobicy.com/jobs?search_keywords=vLLM.md)[SGLang](https://jobicy.com/jobs?search_keywords=SGLang.md)[Cloud infrastructure](https://jobicy.com/jobs?search_keywords=Cloud%20infrastructure.md)[DevOps](https://jobicy.com/jobs?search_keywords=DevOps.md)[Infrastructure automation](https://jobicy.com/jobs?search_keywords=Infrastructure%20automation.md)[Terraform](https://jobicy.com/jobs?search_keywords=Terraform.md)[CI/CD](https://jobicy.com/jobs?search_keywords=CICD.md)[Network troubleshooting](https://jobicy.com/jobs?search_keywords=Network%20troubleshooting.md)[Customer engineering](https://jobicy.com/jobs?search_keywords=Customer%20engineering.md) Sample interview questionsDescribe how you would approach a customer whose LLM inference workload has poor throughput and inconsistent latency on a Kubernetes GPU cluster.I would first establish baseline metrics for request rate, token throughput, queueing, GPU utilization, memory use, and tail latency. I would then validate the serving configuration, model parallelism, batching strategy, KV-cache settings, scheduler behavior, network path, and storage dependencies. After identifying the highest-impact bottleneck, I would test targeted changes, compare results against the baseline, and document a repeatable deployment configuration. How have you translated an ambiguous customer problem into infrastructure requirements? I start by clarifying the user outcome, workload shape, reliability target, security constraints, scale expectations, and current failure modes. I convert these findings into measurable requirements such as GPU count, latency objectives, isolation boundaries, observability needs, and deployment constraints. I review assumptions with the customer and internal teams, then validate the requirements through a prototype or technical evaluation. What factors would you consider when selecting a model-serving stack such as vLLM, SGLang, TensorRT-LLM, or TGI? I would consider model compatibility, hardware platform, batching and scheduling behavior, quantization support, throughput and latency targets, operational maturity, observability, security requirements, and integration with the customer’s existing platform. I would benchmark representative traffic rather than relying solely on published results. The final recommendation should balance performance with reliability, maintainability, and the customer’s ability to operate the system. Give an example of how you would debug a multi-GPU training or inference failure. I would narrow the issue using logs, GPU metrics, container events, and distributed-framework diagnostics. I would check driver and CUDA compatibility, NCCL configuration, topology, network connectivity, GPU memory pressure, resource limits, and scheduler placement. I would reproduce the failure with the smallest viable workload, test one hypothesis at a time, and package the confirmed fix into automation or operational guidance. How would you decide whether a customer request should become a product investment rather than a one-off solution? I would assess whether the problem occurs across multiple customers, has a clear and meaningful business impact, and can be solved with a reusable product capability. I would compare existing alternatives, estimate implementation and support costs, and validate demand through prototypes or structured customer feedback. A strong investment case includes measurable evidence of adoption potential, product fit, and a defined initial scope. 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 Nebius is hiring a Forward Deployed Engineer to work alongside Physical AI customers, shape our core infrastructure products, and test ideas for what we should build next. You will work with enterprises, ISVs, and digital-native companies running training, simulation, synthetic data, evaluation, and model serving workloads. By understanding their systems and helping them reach production, you will uncover requirements that inform our Cloud, Serverless, and Token Factory teams. The goal is to connect needs across Physical AI industries with product decisions, including improvements that benefit enterprise customers more broadly. You will also investigate opportunities for new Nebius products. You will develop hypotheses about unmet needs, examine demand and existing alternatives, and build prototypes to test with customers. Your findings will help determine which opportunities deserve investment and what an initial product should deliver. The work is hands-on. You will write code, deploy and benchmark models, build agents, and troubleshoot infrastructure alongside customer engineers. The position is part of the Physical AI go-to-market team, with close collaboration across customers, NVIDIA, Product, and Engineering. ### What you will do * Work with customer engineers to understand their workloads, infrastructure constraints, and production requirements. * Translate Physical AI use cases into requirements that Cloud, Serverless, and Token Factory teams can use, backed by measurements and customer evidence. * Identify enterprise adoption barriers and common needs across industries. Work with Product and Engineering to assess gaps and test improvements with customers. * Build agents that set up, run, and debug Physical AI workloads, connecting them to clusters, schedulers, storage, and model servers. * Deploy and benchmark open-weight models with serving stacks such as vLLM or SGLang, then tune them for customer workloads. * Build prototypes and run technical evaluations to answer specific questions about performance, reliability, and product fit. * Assess demand and existing alternatives for opportunities outside current product teams’ scope, then build where there is a clear market. * Debug across Python, agents, model servers, Kubernetes, GPUs, networking, and storage. Turn successful setups into reusable scripts, deployments, and short guides. ### What we are looking for * Three or more years in software engineering, cloud infrastructure, DevOps, ML infrastructure, or similar, including ownership of a production service, cluster, or platform. * Experience working directly with customers or external engineers to scope problems, run evaluations, and explain technical tradeoffs. * Ability to turn an unfamiliar customer problem into concrete infrastructure requirements. * Practical experience building with LLMs or agents, at work or independently, with an understanding of their internals (e.g., transformers, KV caching, or tool calling) and how to evaluate their outputs. * Familiarity with enterprise infrastructure requirements, including identity and access management, network isolation, security, monitoring, and reliability. * Strong Python and Linux skills: you can read unfamiliar code, automate a setup, and debug from logs and metrics. * Practical experience with containers and Kubernetes, including configuration, networking, storage, and scheduling. * A record of getting useful software working with incomplete requirements, measuring results, and following through with the people using it. ### Helpful experience * Enterprise customer engineering, solutions architecture, consulting, or taking a new product from an initial customer problem to adoption. * Distributed compute such as Ray, Slurm, Spark, or multi-GPU jobs. * NVIDIA GPU workloads, including memory management, drivers, CUDA, NCCL, and monitoring. * Model serving with vLLM, SGLang, TensorRT-LLM, or TGI. * Robotics, simulation, synthetic data, reinforcement learning, world models, or tools such as Isaac Sim, Isaac Lab, Omniverse, and Cosmos. * Infrastructure automation and enterprise integrations, including Terraform, CI/CD, private connectivity, and identity federation. 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/155951-forward-deployed-engineer-physical-ai-infrastructure.md) > Annual salary information is not provided for this position. 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