Developer Advocate - Physical AI & Robotics

About Nebius

Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure. Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.

The Role

We’re looking for a Developer Advocate who can make Nebius the easiest place to run Physical AI and robotics workloads from simulation and synthetic data to training, deployment, and real-time inference. This is an education-first role: you’ll teach, show what’s possible, and help builders succeed—then feed the hard lessons back into product.

You are welcome to work remotely from the United States.

Your responsibilities will include:

• Builder education: Create tutorials, guides, example repos, reference projects, and workshops for robotics + Physical AI teams.
• Show what’s possible: Build demo workloads (simulation → training → inference) and share them via talks, livestreams, blog posts, and docs.
• Hands-on customer help: Partner with lighthouse teams to unblock architectures, performance, and deployments—then turn solutions into reusable patterns.
• Developer feedback loops: Capture recurring friction (tooling, setup, performance, deployment) and translate it into clear product requirements.
• Ecosystem work: Collaborate with key frameworks and platforms (robotics stacks, simulation tools, model runtimes) to create integrations and examples.
• Internal enablement: Equip solutions teams with technical narratives, architecture diagrams, and training so they can support Physical AI workloads confidently.

What success looks like

Builders can go to working robotics/Physical AI workflow on Nebius quickly. Your demos and content become the default starting point for Physical AI teams evaluating GPU cloud.

We expect you to have:

• Strong technical depth in GPU compute / inference / distributed systems (you can debug and explain tradeoffs).
• Familiarity with robotics or Physical AI workflows: simulation, synthetic data, training pipelines, sensor data, deployment constraints, latency/perf needs.
• You enjoy teaching: writing clearly, building practical demos, speaking to technical audiences.
• Comfort operating in ambiguity and moving fast with small teams.

Nice to have

• Experience with simulation and robotics ecosystems (e.g., Isaac Sim, Mujoco, ROS/ROS2) and/or model serving stacks (vLLM/Triton, etc.).
• Performance profiling instincts (latency, throughput, GPU utilization, storage/network bottlenecks).

Practical experience

• 3-5+ years of experience in software development, cloud engineering, DevOps or a similar technical role
• Demonstrated experience with cloud technologies and infrastructure
• Previous work with infrastructure-as-code, containerization and cloud environments
• Experience explaining complex technical concepts