About the RolePretraining gives us a general model. Post-training makes it useful, controllable, safe, and performant in the real world. You will train large pretrained robot models into production-ready systems via fine-tuning, reinforcement learning, steering, human feedback, task specialization, evaluation, and on-robot validation-at scale. Regardless of your initial background, you will grow into becoming a full-stack ML roboticist capable of quickly pinpoint issues on either side of ML or controls, and all the places in between. This is where research meets reality.
You'll be responsible for:- Designing fine-tuning and adaptation strategies for downstream robotic tasks and embodiments
- Developing methods for improving reliability, robustness, and controllability
- Building evaluation frameworks that measure real-world robot performance, not just offline metrics
- Improving inference-time performance (latency, stability, memory footprint) in collaboration with ML infrastructure
- Leveraging techniques such as imitation learning, RL, distillation, synthetic data, and curriculum learning
- Closing the loop between model outputs and physical-world outcomes
You might thrive in this role if you:- Have experience with fine-tuning large models for downstream tasks (RLHF, IL, RL, distillation, domain adaptation, etc.)
- Have worked on embodied AI, robotics, or real-world ML systems
- Care deeply about evaluation, benchmarking, and failure analysis
- Are comfortable debugging across the ML stack - from loss curves to robot behavior
- Enjoy rapid iteration with real-world feedback loops
- Want to bridge the gap between foundation models and physical deployment
