Research Scientist: Post-Training

Generalist AI; San Mateo (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Generalist is seeking a skilled ML roboticist to advance pretrained robot models into production-ready systems. You will fine-tune large models, apply RLHF/IL, and validate in real-world robotic setups, bridging research and deployment.

You'll collaborate with ML infrastructure and robotics teams to optimize latency, robustness, and controllability while expanding evaluation benchmarks and real-world test suites to ensure dependable performance in physical environments.

Qualifications

• Experience with fine-tuning large models for downstream tasks (RLHF, IL, RL, distillation).
• 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 loss curves to robot behavior.
• Enjoy rapid iteration with real-world feedback loops.
• Interest in bridging foundation models and physical deployment.

Responsibilities

• Design fine-tuning and adaptation strategies for downstream robotic tasks.
• Develop methods to improve reliability, robustness, and controllability.
• Build evaluation frameworks measuring real-world robot performance.
• Improve inference-time performance with ML infra collaboration.
• Leverage imitation learning, RL, distillation, synthetic data, curriculum learning.
• Close the loop between model outputs and physical-world outcomes.

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.

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