Research Scientist: Post-Training

Boston (Somerville)full time

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On Site

$240k–$350k

H1B: no

Generalist Research Scientist: Post-Training at Generalist fine-tunes large pretrained robot models for real-world deployment using techniques like reinforcement learning and imitation learning. The role includes building evaluation frameworks and improving inference performance.

Responsibilities

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.
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

Qualification

Experience with embodied AICare about evaluation

Required

Experience fine-tuning large models for downstream tasks (RLHF, IL, RL, distillation, domain adaptation)
Experience with embodied AI, robotics, or real-world ML systems
Care about evaluation, benchmarking, and failure analysis
Comfortable debugging across the ML stack