About the role*Open to candidates based near our NYC office or those willing to relocate.
Building general world models - systems that understand and simulate reality across tasks, modalities, and domains - requires closing the loop between learned representations and real-world action. We're looking for a Robotics Engineer to bring our video-native foundation models onto real hardware: deploying world-model-based policies on real robots and making them work in the real world.
You will work across the full stack of robot learning - from data collection and task design, to policy deployment, to physical evaluation. This is a hands-on, execution-oriented role at the intersection of foundation models, learned robot policies, and hardware. You'll bring deep robotics domain expertise and help us ship world-model-based robot policies end-to-end, with applications ranging from manipulation to mobile robotics.
What you'll do- Own the deployment loop for learned policies (VLAs, diffusion policies, World Action Models) on real robot arms: inference serving, action decoding, controller integration, latency budgets, and safety limits
- Diagnose real-world policy failures - determining whether the problem is data coverage, camera/proprioception mismatch, or execution - and partner with the research team to fix it at the right layer
- Design demonstration data collection protocols (task setup, camera placement, teleop conventions) that produce data our world models can actually learn from - working with our existing teleop and lab infrastructure
- Partner with research to run controlled experiments connecting world model representations, data composition, and fine-tuning recipes to physical success rates
- Define and run physical evaluation protocols so results are trustworthy and comparable across policy iterations
- Adapt our policy recipe to partner hardware and new embodiments
What you'll need- You have personally deployed software on real robot hardware and iterated to make it work - whether learned policies, motion planning, classical control, or perception-driven manipulation. You can walk through a specific system you shipped, what broke on the physical robot that didn't break in sim or testing, and how you fixed it.
- Fluency in the software-to-robot interface: action/command spaces, control frequencies, observation pipelines, calibration, and how each affects real-world behavior
- Comfort working at the intersection of software and physical systems: you can reconfigure a robot workspace and trace a policy failure in the same afternoon
- Bonus: direct experience deploying learned policies (VLAs, diffusion policies) on real hardware; experience with video/multimodal generative models or world models
Runway strives to recruit and retain exceptional talent from diverse backgrounds while ensuring pay equity for our team. Our salary ranges are based on competitive market rates for our size, stage and industry, and salary is just one part of the overall compensation package we provide.
There are many factors that go into salary determinations, including relevant experience, skill level and qualifications assessed during the interview process, and maintaining internal equity with peers on the team. The range shared below is a general expectation for the function as posted, but we are also open to considering candidates who may be more or less experienced than outlined in the job description. In this case, we will communicate any updates in the expected salary range.
Lastly, the provided range is the expected salary for candidates in the U.S. Outside of those regions, there may be a change in the range, which again, will be communicated to candidates.
