Research Scientist: Post-Training

• 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

• Experience with fine-tuning large models for downstream tasks (RLHF, IL, RL, distillation, domain adaptation, etc.)
• Worked on embodied AI, robotics, or real-world ML systems
• Care deeply about evaluation, benchmarking, and failure analysis
• 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

Offers Equity