Note: The job is a remote job and is open to candidates in USA. Nebius is leading a new era in cloud infrastructure for the global AI economy, building a full-stack AI cloud platform. The Forward Deployed Engineer, Physical AI Systems is a senior role that bridges customer data, AI models, and real-world deployment feedback, owning technical execution and contributing to the Physical AI roadmap.
Responsibilities
- End-to-End Ownership Inside Strategic Accounts: Own discovery, technical scoping, system design, build, and production rollout for each design partner and ISV engagement. Partner directly with customer engineering and domain teams to translate ambiguous problems into deployable production systems
- Physical AI Workflows & Pipelines: Build and own physical AI workflows across real-world data, synthetic data, model training, evaluation, deployment, and failure capture. Develop practical ML and evaluation pipelines for perception, autonomy, world models, and policy-learning use cases, operating inside the customer's codebase, on their infrastructure, against their data
- Evaluation & Failure Loops: Design scenario-based evaluation workflows, regression testing, failure analysis, and before-versus-after model comparisons. Help define the real-to-sim-to-real and failure-to-retrain loops, and convert customer failures into product insights, datasets, scenarios, tests, and retraining loops
- Customer Data & Integration: Work with customer datasets including video, images, telemetry, annotations, simulation outputs, and deployment logs. Integrate NVIDIA ecosystem tools where useful, including Isaac Sim, Isaac Lab, Cosmos, NeMo, GR00T, and Jetson, and decide where the platform should build, buy, or integrate across labeling, synthetic data, simulation, training, evaluation, and monitoring
- ISV Integration Development: Stand up custom technical integrations with key Physical AI ecosystem partners (simulation frameworks, robotics toolchains, data management vendors). Build the reference architectures and joint solutions that turn ISV partnerships into deployable, repeatable assets
- Pattern Codification & Productization: Identify which prototypes contain generalizable abstractions worth hardening into modular product components. Partner with the Field CTO, Product, and Engineering teams to fold these into the core Physical AI platform. Treat every engagement as a forcing function for the next ten
- Rapid Engineering Velocity: Use modern AI coding tools (Claude Code, Codex, Cursor) as primary leverage. Compress prototype timelines from weeks to days. Treat engineering velocity as a primary success metric; outpacing customer expectations on time-to-working-code is the competitive moat of this role
- Field Enablement Contributions: Co-author high-value technical artifacts (reference architectures, solution templates, technical blogs) that arm the broader Nebius SA and sales field to scale Physical AI sales beyond our direct engagements
- Feedback Loops to Product: Maintain structured channels to ensure customer learnings (what worked, what broke, where the platform fell short) flow back to the Field CTO, Product, and Engineering teams. You are part of the company's primary product discovery mechanism
- Technical Eminence: Represent Nebius at customer engineering deep-dives, ISV technical co-build sessions, and industry events (CVPR, CoRL, ICRA, RoboBusiness, NeurIPS workshops). Build credibility as someone who has actually shipped working code in the domain
Skills
- 6+ Years of Hands-On Engineering: Experience in applied ML, physical AI, computer vision, robotics, simulation, autonomy, or AI/ML platforms, with at least two years in a customer-facing or deployment-oriented technical role (Forward Deployed Engineer, founding engineer, technical co-founder, tech lead embedded with strategic customers, or equivalent)
- Real ML Systems Beyond Notebooks: Demonstrated track record building data pipelines, training pipelines, evaluation harnesses, model versioning, deployment, and monitoring that real users have depended on at meaningful scale, not just demos or proofs-of-concept that fall apart in real environments
- Strong Python & ML Frameworks: Strong Python engineering skills and hands-on experience with PyTorch or similar ML frameworks
- Evaluation & Model Improvement Instinct: Practical understanding of evaluation, metrics, failure analysis, data quality, and model improvement loops, with the ability to reason about sim-to-real gaps, domain shift, edge cases, and production reliability
- Multimodal Data Fluency: Experience working with video, image, telemetry, annotation, and multimodal datasets
- AI-Native Development Workflow: You are fluent in modern AI coding tools (Claude Code, Codex, Cursor) and treat them as primary leverage to rapidly design, implement, test, debug, and refactor production-quality software, shipping significantly faster than traditional engineering workflows allow
- GPU & Distributed Systems Fluency: Strong working knowledge of GPU compute, distributed training infrastructure, high-throughput storage systems, and orchestration frameworks (Kubernetes, Ray, Slurm, etc.)
- Customer-Facing Engineering Maturity: Comfort working directly inside customer environments, shipping code on someone else's infrastructure, navigating their codebase, and earning credibility with their engineers and CTO in days, not months
- Prototype Mindset: Strong instinct for the 80/20 of prototype engineering: knowing what to build fast, what to throw away, and what to harden. A bias toward real, useful systems over research demos, and comfort holding ambiguity and making decisions with incomplete information
- High Agency: You navigate ambiguity in complex organizations without waiting for permission. You are biased toward shipping over scheduling another meeting
- Communication: Strong written and verbal communication skills. You can hold your own in a technical conversation with a customer CTO, debrief a design partner engagement to the Head of Physical AI, and write a technical blog that holds up to scrutiny on Hacker News
- 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 such as MuJoCo, Drake, FiftyOne, MCAP, and Foxglove
- 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 (e.g., NVIDIA Isaac, MuJoCo, Drake, ROS, FiftyOne, MCAP, Foxglove)
Benefits
- Health Insurance: 100% company-paid medical, dental, and vision coverage for employees and families.
- 401(k) Plan: Up to 4% company match with immediate vesting.
- Parental Leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.
- Remote Work Reimbursement: Up to $85/month for mobile and internet.
- Disability & Life Insurance: Company-paid short-term, long-term, and life insurance coverage.
Company Overview