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Forward Deployed Engineer, Physical AI Infrastructure

Nebius· Robotics & AI
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RemotePosted 4w ago

About the role

Nebius is hiring a Forward Deployed Engineer to work with Physical AI customers, shape core infrastructure products, and test new ideas. You will interact with enterprises, ISVs, and digital-native companies to understand workloads, translate use cases into requirements, build prototypes, and evaluate product fit. The role is hands-on, involving coding, deploying and benchmarking models, building agents, and troubleshooting infrastructure alongside customer engineers.

Responsibilities

  • 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.

Qualifications

  • 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, with 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: ability to 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.

Skills

Required:Customer Engineering/Stakeholder CollaborationInfrastructure Requirements TranslationLLMs and AgentsPythonLinuxContainers & KubernetesPerformance Benchmarking

Benefits

  • 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
Last updated Oct 7, 2026. Interested? Apply on the company site.
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About Nebius

The ultimate cloud for AI explorers

Discover the most efficient way to build, tune and run your AI models and applications on top-notch NVIDIA® GPUs.

AI CloudToken FactoryGPUKubernetesSlurmManaged servicesTerraformAPI
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