Nebius
Forward Deployed Engineer, Physical AI Infrastructure
$180K–$224K
Remote - United States · Remote · Full-time · Posted Jul 6, 2026
- Experience
- Three or more years; no alternative stated.
- Visa sponsorship
- Applicants must be authorized to work in the country in which they apply and provide proof of employment eligibility as a condition of hire.
About the job
Works with Physical AI customers to deploy and benchmark workloads, troubleshoot infrastructure, translate customer needs into product requirements, and prototype potential products.
What you'll do
- Assess customer workloads and production constraints, and turn findings into evidence-backed requirements and product improvements.
- Build agents and prototypes; deploy, benchmark, and tune models and debug customer infrastructure.
- Evaluate unmet market needs and alternatives, then test product opportunities with customers.
What they ask 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.
- Practical experience building with LLMs or agents and understanding their internals and evaluation; strong Python and Linux skills.
- Practical experience with containers and Kubernetes, plus familiarity with enterprise infrastructure requirements such as access management, network isolation, security, monitoring, and reliability.
Nice to have
- Enterprise customer engineering, solutions architecture, consulting, or taking a product from an initial customer problem to adoption.
- Experience with distributed compute, NVIDIA GPU workloads, model serving, Physical AI, or infrastructure automation and enterprise integrations.
Pay
$179,500 — $224,300 USD (base salary)
Tools
Python, Linux, Kubernetes, Ray, Slurm, Spark, vLLM, SGLang
AI focus
Deploying, benchmarking, and tuning models; building agents for Physical AI workloads; evaluating AI infrastructure and potential products.
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