Research Scientist: Post-Training

Generalist; Somerville, San Mateo (MA, CA); On-site; USD 100,000 - 130,000; Full time

Job summary

Generalist in Somerville seeks a skilled ML roboticist to train large pretrained models into production-ready systems. The role involves designing adaptation strategies and developing reliable methods for robotics, focusing on real-world metrics.

The ideal candidate should have experience with fine-tuning models, work in robotics, and be comfortable debugging across the ML stack. Equal opportunity employer with a commitment to diversity.

Qualifications

• Experience with fine-tuning for downstream tasks.
• Worked on embodied AI, robotics, or real-world ML systems.
• Care about evaluation, benchmarking, and failure analysis.

Responsibilities

• Design fine-tuning and adaptation strategies.
• Develop methods for reliability and robustness.
• Build evaluation frameworks for real-world performance.
• Improve inference-time performance.
• Leverage techniques like imitation learning and synthetic data.

Skills

Experience with fine-tuning large models

Understanding of reinforcement learning

Experience in robotics

Debugging across the ML stack

Job description

About the Role

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

• Are 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

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.