Research Scientist: Post-Training

Generalist • San Francisco, CA

2d

About The Position

Pretraining 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.

Requirements

• 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

Responsibilities

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed