Research Scientist: Post-Training

Generalist AI Inc.; Boston (MA); On-site; USD 140,000 - 230,000; Full time

Job summary

Generalist AI, Inc. in the United States is seeking a full-time ML roboticist to help train large pretrained robot models into production-ready systems. You will work across fine-tuning, RL-based steering, and evaluation against real-world tasks.

Ideal candidates have hands-on experience with embodied AI, robotics, and real-world ML systems, and can improve latency, robustness, and controllability while bridging foundation models and physical deployment.

Qualifications

• Experience fine-tuning large models for downstream tasks (RLHF, IL, RL).
• Background in embodied AI, robotics, or real-world ML systems.
• Strong evaluation, benchmarking, and failure analysis.
• Ability to debug across ML stack from losses to robot behavior.

Responsibilities

• 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

Skills

Fine-tuning large models

Robotics expertise

Evaluation & benchmarking

Debug ML stack

PyTorch

RL frameworks

Experiment tracking

Tools

PyTorch

RL frameworks

Experiment tracking

Job description

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