Forward Deployed Engineer - Physical AI

Role Description

Nebius is building the cloud infrastructure that will power the next generation of Physical AI: robotics, autonomous systems, simulation, world models, and embodied intelligence operating in the real world.

The Forward Deployed Engineer, Physical AI Systems is a senior, high-autonomy individual contributor role that owns the technical bridge between customer data, AI models, simulation workflows, evaluation systems, and real-world deployment feedback. This role sits with strategic customers and ISV partners, embedded directly inside their engineering teams, and ships production software that turns messy real-world physical AI problems into reliable, measurable platform workflows.

You will work alongside the Field CTO and the Head of Physical AI. Inside each account, you own end-to-end technical execution:

• Discovery
• Scoping
• Model and evaluation pipeline design
• Build and production rollout

Across accounts, you identify the patterns worth productizing and then partner with Product and Engineering to fold them into the core platform. Your field work is the primary input to the Nebius Physical AI roadmap, and your job is to prove the platform's core claim: that customers can move from real-world failures to measurable model improvement faster than their internal tools allow, repeatably and at scale.

We are looking for engineers with the seniority and judgment of a founding engineer or staff individual contributor, people who can show up at a robotics company or a world model lab on a Monday and have credibility with the CTO by Friday. You will be trusted to make consequential technical decisions in ambiguous environments without waiting for permission.

In return, you get founder-level autonomy in an IC seat, direct exposure to the most important companies defining Physical AI, and the resources of a public cloud platform behind every line of code you ship. This is a definitive zero-to-one opportunity to write the code that defines the highest-growth segment of AI.

You are welcome to work remotely from the United States (SF Bay Area, CA or Austin, TX preferred).

Qualifications

• 6+ Years of Hands-On Engineering experience in applied ML, physical AI, computer vision, robotics, simulation, autonomy, or AI/ML platforms.
• Demonstrated track record building data pipelines, training pipelines, evaluation harnesses, model versioning, deployment, and monitoring that real users have depended on at meaningful scale.
• Strong Python engineering skills and hands-on experience with PyTorch or similar ML frameworks.
• Practical understanding of evaluation, metrics, failure analysis, data quality, and model improvement loops.
• Experience working with video, image, telemetry, annotation, and multimodal datasets.
• Fluency in modern AI coding tools (Claude Code, Codex, Cursor).
• Strong working knowledge of GPU compute, distributed training infrastructure, high-throughput storage systems, and orchestration frameworks (Kubernetes, Ray, Slurm, etc.).
• Comfort working directly inside customer environments, shipping code on someone else's infrastructure.
• Strong instinct for the 80/20 of prototype engineering.
• Ability to navigate ambiguity in complex organizations without waiting for permission.
• Strong written and verbal communication skills.

Requirements

• Prior experience as a Forward Deployed Engineer or an equivalent customer-embedded engineering function at a frontier company.
• Background as a founding engineer or technical co-founder at a robotics, simulation, autonomous systems, or foundation-model company.
• Experience with robotics, drones, industrial automation, warehouse robotics, inspection, or autonomous systems.
• Familiarity with Isaac Sim, Isaac Lab, Omniverse, Cosmos, NeMo, GR00T, ROS2, Jetson, edge deployment, or related tooling.
• Experience with synthetic data generation, scenario generation, or simulation-based evaluation.
• Experience with world models, vision-language-action models, policy learning, reinforcement learning, or representation learning.
• Experience building model evaluation platforms, golden datasets, regression testing systems, or failure clustering systems.
• Open-source contributions to relevant Physical AI ecosystem projects.

Benefits

• 100% company-paid medical, dental, and vision coverage for employees and families.
• 401(k) Plan: Up to 4% company match with immediate vesting.
• 20 weeks paid parental leave for primary caregivers, 12 weeks for secondary caregivers.
• Up to $85/month for mobile and internet reimbursement.
• Company-paid short-term, long-term, and life insurance coverage.