Research Scientist: Post-Training

Generalist • San Francisco, CA

4d

About The Position

Generalist is building general intelligence for the physical world, aiming to make it useful for everyone. The company focuses on embodied foundation models, starting with dexterity, and integrates large-scale AI and robotics. The team comprises researchers and roboticists from leading AI and robotics labs, with a history of shipping AI breakthroughs and developing advanced robots and AI models. Generalist is an equal opportunity employer.

The Post-Training Research Scientist role focuses on making large pretrained robot models useful, controllable, safe, and performant in real-world applications. This involves fine-tuning, reinforcement learning, steering, human feedback, task specialization, evaluation, and on-robot validation at scale. The role offers the opportunity to become a full-stack ML roboticist, capable of diagnosing issues across ML and controls, bridging the gap between research and practical deployment.

Requirements

• Experience with fine-tuning large models for downstream tasks (RLHF, IL, RL, distillation, domain adaptation, etc.)
• Experience working 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