Job description

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. 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 $179,500 — $224,300 USD 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.

Requirements

Must-haves

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.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlgopythonkubernetesllmci/cd

Platform: greenhouse
Effort: quick