{"id":2242335,"url":"https://alion.io/job/nebius-forward-deployed-engineer-physical-ai-infrastructure","title":"Forward Deployed Engineer, Physical AI Infrastructure","company":{"id":6364,"name":"Nebius","domain":"nebius.com","url":"https://alion.io/company/nebius","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"Solutions","role_family":"Solutions","seniority":"middle","employment_type":null,"work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["DE","FR","GB","AT","BE","BG","HR","CZ","DK","FI","HU","IE","IT","NL","NO","PL","RO","ES","SE","CH","EE","GR","IS","LV","LI","LT","LU","MT","PT","SK","SI"],"hiring_countries_total":31,"salary":null,"salary_estimate":{"min_usd":98000,"max_usd":262000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":736},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Function Calling","optional":false},{"name":"Isaac Lab","optional":false},{"name":"Isaac Sim","optional":false},{"name":"Kubernetes","optional":false},{"name":"Linux","optional":false},{"name":"NCCL","optional":false},{"name":"Physical AI","optional":false},{"name":"Python","optional":false},{"name":"Ray","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"SGLang","optional":false},{"name":"SLURM","optional":false},{"name":"Spark","optional":false},{"name":"Synthetic Data","optional":false},{"name":"TensorRT-LLM","optional":false},{"name":"Terraform","optional":false},{"name":"TGI","optional":false},{"name":"Tool Use","optional":false},{"name":"vLLM","optional":false},{"name":"World Models","optional":false},{"name":"TensorRT","optional":true}],"status":"live","first_seen_at":"2026-09-17T12:09:17Z","employer_posted_date":"2026-10-08","last_verified_at":"2026-10-12T02:40:44Z","board_verified":true,"closed_at":null,"days_open":24,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":24},"description":"About Nebius:\nNebius 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.\nBuilt by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.\nListed 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.\nThe role\nNebius 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.\nYou 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.\nYou 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.\nThe work is hands-on. You will write code, deploy and benchmark models, build agents, and troubleshoot infrastructure alongside customer engineers.\nThe position is part of the Physical AI go-to-market team, with close collaboration across customers, NVIDIA, Product, and Engineering.\nWhat you will do\nWork with customer engineers to understand their workloads, infrastructure constraints, and production requirements.\nTranslate Physical AI use cases into requirements that Cloud, Serverless, and Token Factory teams can use, backed by measurements and customer evidence.\nIdentify enterprise adoption barriers and common needs across industries. Work with Product and Engineering to assess gaps and test improvements with customers.\nBuild agents that set up, run, and debug Physical AI workloads, connecting them to clusters, schedulers, storage, and model servers.\nDeploy and benchmark open-weight models with serving stacks such as vLLM or SGLang, then tune them for customer workloads.\nBuild prototypes and run technical evaluations to answer specific questions about performance, reliability, and product fit.\nAssess demand and existing alternatives for opportunities outside current product teams’ scope, then build where there is a clear market.\nDebug across Python, agents, model servers, Kubernetes, GPUs, networking, and storage. Turn successful setups into reusable scripts, deployments, and short guides.\nWhat we are looking for\nThree or more years in software engineering, cloud infrastructure, DevOps, ML infrastructure, or similar, including ownership of a production service, cluster, or platform.\nExperience working directly with customers or external engineers to scope problems, run evaluations, and explain technical tradeoffs.\nAbility to turn an unfamiliar customer problem into concrete infrastructure requirements.\nPractical 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.\nFamiliarity with enterprise infrastructure requirements, including identity and access management, network isolation, security, monitoring, and reliability.\nStrong Python and Linux skills: you can read unfamiliar code, automate a setup, and debug from logs and metrics.\nPractical experience with containers and Kubernetes, including configuration, networking, storage, and scheduling.\nA record of getting useful software working with incomplete requirements, measuring results, and following through with the people using it.\nHelpful experience\nEnterprise customer engineering, solutions architecture, consulting, or taking a new product from an initial customer problem to adoption.\nDistributed compute such as Ray, Slurm, Spark, or multi-GPU jobs.\nNVIDIA GPU workloads, including memory management, drivers, CUDA, NCCL, and monitoring.\nModel serving with vLLM, SGLang, TensorRT-LLM, or TGI.\nRobotics, simulation, synthetic data, reinforcement learning, world models, or tools such as Isaac Sim, Isaac Lab, Omniverse, and Cosmos.\nInfrastructure automation and enterprise integrations, including Terraform, CI/CD, private connectivity, and identity federation.\nBenefits & Perks:\nCompetitive compensation\nCareer growth and learning opportunities\nFlexibility and ownership\nCollaborative and innovative culture\nOpportunity to work on impactful AI projects\nInternational environment and talented teams\nWhat's it like to work at Nebius:\nFast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI \nEqual Opportunity Statement:\nNebius 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.\nApplicants 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. \nIf you need accommodations during the application process, please let us know.","description_format":"text","description_chars":6066,"description_truncated":false,"requirements":{"experience_years_min":3,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"Germany","iso":"DE","kind":"region"},{"name":"France","iso":"FR","kind":"region"},{"name":"United 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