Developer Advocate - Physical AI & Robotics
NebiusRemote - United StatesPosted 12 March 2026
Job Description
Why work at Nebius Nebius is leading a new era in cloud computing to serve the global AI economy. We create the tools and resources our customers need to solve real-world challenges and transform industries, without massive infrastructure costs or the need to build large in-house AI/ML teams. Our employees work at the cutting edge of AI cloud infrastructure alongside some of the most experienced and innovative leaders and engineers in the field.
Where we work Headquartered in Amsterdam and listed on Nasdaq, Nebius has a global footprint with RD hubs across Europe, North America, and Israel. The team of over 800 employees includes more than 400 highly skilled engineers with deep expertise across hardware and software engineering, as well as an in-house AI RD team.
The Role
We’re looking for a Developer Advocat e 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 to different audiences through writing, speaking, or training
Prior involvement in developer communities, open-source projects or technical forums (desirable)
Communication and interpersonal skills
Excellent verbal and written communication abilities, with a talent for making complex technical concepts accessible
Comfortable with public speaking and delivering technical presentations to diverse audiences
Ability to build relationships with technical communities and individual developers
Strong collaboration skills for working across product, engineering and marketing teams
Self-direction and initiative in a rapidly evolvi ... (truncated, view full listing at source)
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