Forward Deployed Engineer, Physical AI Infrastructure
$180k - $224k • Remote - United States
Posted 5d ago
Job Location
Remote - United States
Tech Stack
Remote Work Policy
On-site
Categories
AI Infrastructure Engineer
About the job
Nebius is building a full-stack AI cloud platform for the global AI economy, supporting developers and enterprises from data and model training through to production deployment. As a Forward Deployed Engineer, you will work directly with Physical AI customers to understand their workloads and production requirements. Your insights will shape our core infrastructure products and inform future development by connecting needs across Physical AI industries with product decisions.
You will also investigate opportunities for new Nebius products by developing hypotheses about unmet needs, examining demand, and building prototypes to test with customers. This hands-on role involves writing code, deploying and benchmarking models, building agents, and troubleshooting infrastructure alongside customer engineers, with close collaboration across customers, NVIDIA, Product, and Engineering.
Responsibilities
- Collaborate with customer engineers to understand workloads, infrastructure constraints, and production requirements.
- Translate Physical AI use cases into actionable requirements for Cloud, Serverless, and Token Factory teams, supported by data and customer evidence.
- Identify enterprise adoption barriers and common needs across industries, working with Product and Engineering to assess gaps and test improvements.
- Build agents to set up, run, and debug Physical AI workloads, integrating with clusters, schedulers, storage, and model servers.
- Deploy and benchmark open-weight models using serving stacks like vLLM or SGLang, and tune them for specific customer workloads.
- Develop prototypes and conduct technical evaluations to assess performance, reliability, and product fit.
- Assess market demand and existing alternatives for new product opportunities, and build prototypes where a clear market exists.
- Debug issues across Python, agents, model servers, Kubernetes, GPUs, networking, and storage, and create reusable scripts and guides from successful setups.
Requirements
- Three or more years of experience in software engineering, cloud infrastructure, DevOps, or ML infrastructure, including ownership of a production service, cluster, or platform.
- Experience working directly with customers or external engineers to define problems, conduct evaluations, and explain technical trade-offs.
- Ability to translate unfamiliar customer problems into concrete infrastructure requirements.
- Practical experience building with LLMs or agents, understanding their internals (e.g., transformers, KV caching, tool calling) and evaluation methods.
- Familiarity with enterprise infrastructure requirements such as identity and access management, network isolation, security, monitoring, and reliability.
- Strong Python and Linux skills for code reading, automation, and debugging.
- Practical experience with containers and Kubernetes, including configuration, networking, storage, and scheduling.
- Proven ability to deliver functional software with incomplete requirements, measure results, and follow through with users.