Research Scientist: Post-Training

Generalist; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Generalist in San Francisco is seeking a talented individual to contribute to the development of general-purpose robots. In this role, you will design fine-tuning and adaptation strategies for various robotic tasks, while also developing methods to enhance reliability and controllability.

Your background in fine-tuning large models and embodied AI is essential, as you will have the opportunity to apply your skills in a dynamic environment that bridges research with real-world applications. Join us on our mission to revolutionize how humans and machines collaborate.

Qualifications

• Experience with RLHF, IL, RL, distillation, and domain adaptation.
• Comfortable debugging and evaluating performance of ML systems.

Responsibilities

• Design fine-tuning and adaptation strategies for robotic tasks.
• Develop methods to improve reliability and controllability.
• Build evaluation frameworks for real-world performance.
• Improve inference-time performance in collaboration with ML infrastructure.
• Leverage techniques such as imitation learning and synthetic data.

Skills

Experience with fine-tuning large models

Embodied AI experience

Evaluation and benchmarking

Debugging across the ML stack

Rapid iteration with feedback loops

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

About Generalist

At Generalist, we are on a mission to make general-purpose robots a reality. 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.