Forward Deployed Engineer (Physical AI)

Location: Remote in Europe; remote work from the United States is also welcomed, with San Francisco Bay Area or Austin, Texas preferred.

Company

Nebius builds a full-stack AI cloud platform for data processing, model training, inference, and production deployment.

What you will do

• Own discovery, technical scoping, infrastructure design, implementation, and production rollout for strategic customer and ISV engagements.
• Build and operate cloud infrastructure for simulation, training, evaluation, inference, and batch workloads.
• Develop platform services for job execution, scheduling, retries, observability, logging, secrets, access control, and cost tracking.
• Create secure, isolated, observable onboarding environments with dataset storage, workflow execution, and deployment capabilities.
• Optimize reliability, security, performance, utilization, and cloud costs while debugging application, network, storage, compute, and orchestration issues.
• Convert recurring customer infrastructure challenges into reusable platform capabilities, reference architectures, and core product improvements.

Requirements

• 6+ years of hands-on backend, cloud infrastructure, platform engineering, or SRE experience, including at least 2 years in a customer-facing or deployment-oriented technical role.
• Experience building distributed systems, job orchestration, compute platforms, internal developer platforms, or ML infrastructure.
• Strong Python, Go, or similar systems and backend programming skills.
• Experience with Kubernetes, containers, CI/CD, observability, cloud networking, storage, IAM/RBAC, and infrastructure as code.
• Familiarity with GPU workloads, batch jobs, training pipelines, inference workloads, or HPC-style compute environments.
• Strong cross-layer debugging, security, reliability, communication, and autonomous decision-making skills.

Nice to have

• Experience as a Forward Deployed Engineer or in an equivalent customer-embedded engineering role.
• Experience with Nebius, AWS, GCP, Azure, Lambda Labs, or other AI cloud infrastructure.
• Experience with Slurm, Kubernetes GPU scheduling, Ray, Argo, Airflow, Metaflow, or similar orchestration tools.
• Experience with ML training infrastructure, model serving, simulation workloads, large-scale data pipelines, or enterprise production pilots.
• Familiarity with NVIDIA GPU infrastructure, CUDA, Isaac Sim, or Omniverse.

Culture & Benefits

• Competitive compensation and career growth opportunities.
• Flexibility, ownership, and a fast-moving engineering environment.
• Collaborative, international teams working on impactful AI projects.
• Opportunities to shape AI infrastructure and contribute to the future of physical AI.