Research Scientist: Post-Training

Generalist•Somerville, MA

About The Position

Generalist is building embodied foundation models to enable robots to intelligently interact with the physical world, with an initial focus on dexterity. The Research Scientist: Post-Training will turn large pretrained robot models into useful, controllable, safe, and performant production-ready systems through fine-tuning, reinforcement learning, steering, human feedback, task specialization, evaluation, and on-robot validation at scale. The role involves working across machine learning and robotics, diagnosing issues from model training through controls and physical execution, and using real-world feedback to improve deployed systems.

Requirements

  • Experience fine-tuning large models for downstream tasks, such as through RLHF, imitation learning, reinforcement learning, distillation, or domain adaptation.
  • Experience with embodied AI, robotics, or real-world machine learning systems.
  • Care about evaluation, benchmarking, and failure analysis.
  • Comfort debugging across the machine learning 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

  • Design fine-tuning and adaptation strategies for downstream robotic tasks and embodiments.
  • Develop methods to improve reliability, robustness, and controllability.
  • Build evaluation frameworks that measure real-world robot performance, not only offline metrics.
  • Improve inference-time performance, including latency, stability, and memory footprint, in collaboration with ML infrastructure.
  • Apply techniques such as imitation learning, reinforcement learning, distillation, synthetic data, and curriculum learning.
  • Close the loop between model outputs and physical-world outcomes.
  • Validate post-trained models on robots and iterate based on real-world feedback.
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