Member of Technical Staff, Robotics Research Engineer

About the role

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 Research Engineer to own the robotics vertical of our world models: taking our video-native foundation models and turning them into policies that control real robots in the real world. You will work across the full stack of robot learning — from data collection and task design, to policy training, to physical evaluation and deployment. 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

• Design and execute end-to-end robot learning pipelines — from task design and demonstration data collection through policy training, and physical evaluation
• Deploy and iterate on learned policies (VLAs, diffusion policies, World Action Models) on real robot hardware, closing the loop between model predictions and physical outcomes
• Run controlled experiments to understand how world model representations, data composition, and fine-tuning strategies translate to downstream manipulation and locomotion performance
• Build and maintain physical evaluation benchmarks and infrastructure — designing tasks, procuring hardware, calibrating systems, and measuring real-world success rates
• Coordinate robot data collection efforts across internal teams and external partners, ensuring data quality, coverage, and consistency across embodiments
• Partner with the world model research team to translate model capabilities into concrete robotics applications, identifying where our video foundation models unlock new robot behaviors
• Identify and resolve bottlenecks across the robotics stack — whether in data, training infrastructure, hardware configuration, or evaluation methodology — to keep the overall system moving fast

What you’ll need

• Hands-on robotics experience spanning data collection, model training, and physical evaluation. Direct experience with modern learned policies (e.g., VLAs, diffusion policies) on real hardware.
• Experience with robot data collection, teleoperation, and demonstration pipelines across at least one manipulation or mobile platform
• Strong intuition for the full robot learning lifecycle: task design → data collection → policy training → physical evaluation
• Comfort working across software, hardware, and physical systems — you can debug a training run and reconfigure a robot workspace in the same afternoon
• Proficiency with at least one ML framework (e.g., PyTorch, JAX)

Bonus:

• Experience with video or multimodal generative models, world models, or using foundation model representations for downstream control

Compensation

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. 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.