Research Scientist: Post-Training

Generalist; Medford Hillside (MA); On-site; USD 180,000 - 240,000; Full time

Job summary

Generalist is building general intelligence for the physical world, focusing on dexterity and production-ready robotic systems. You will train large pretrained robot models into scalable, production-ready implementations through fine-tuning, RL, and human feedback, bridging research and real-world deployment.

Joining requires expertise in fine-tuning, embodied robotics, and robust evaluation. You’ll work across ML and controls, improving reliability and performance in real-world tasks and

Qualifications

• Experience fine-tuning large models for downstream tasks (RLHF, IL, RL, distillation, domain adaptation).
• Background in embodied AI, robotics, or real-world ML systems.
• Strong focus on evaluation, benchmarking, and failure analysis.
• Ability to debug across the ML stack from training curves to robot behavior.
• Comfort with rapid iteration using real-world feedback loops.

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, beyond offline metrics.
• Improve inference-time performance (latency, stability, memory footprint) with ML infrastructure collaboration.
• Leverage imitation learning, RL, distillation, synthetic data, and curriculum learning.
• Close the loop between model outputs and physical-world outcomes.

Skills

Fine-tuning large models

Embodied AI experience

Robotics/ML systems

Evaluation & benchmarking

Debugging ML stacks

Rapid iteration & productionization

Job description

About Generalist

At Generalist, we are on a mission to build general intelligence for the physical world and make it useful to everyone. We believe the industries and homes of the future will depend on humans and machines working together in new ways. Robots can help us build more and get more done.

We build embodied foundation models, starting with a focus on dexterity. This requires advancing the frontiers of data, models, and hardware, to enable robots to intelligently interact with the physical world.

The company embraces both large-scale AI and robotics as core to its DNA. Our team of researchers, roboticists, and company builders come from OpenAI, Boston Dynamics, Google DeepMind, and other frontier labs—with a track record of shipping AI breakthroughs. Before Generalist, we pioneered large embodied multimodal models and vision-language-action models (PaLM-E, RT-2, Gemini Robotics), launched and scaled ChatGPT and GPT-4 to hundreds of millions of users, engineered the foundations of autonomous driving, built next-generation robots (Atlas, Spot, Stretch) and pushed the limits of what they can do (from parkour to manipulation, and testing robustness).

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.

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