Research Resident

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:

We are looking for a Research Resident to help design, run, and analyze experiments at the intersection of machine learning and robotics. This is an entry-level research role for individuals with less than 3 years of machine learning research experience, and is designed to be a potential career path towards eventually contributing as a Research Scientist.

At Generalist, we are building foundation models for robots. These models improve through a tight feedback loop: design experiments, collect data, train or fine-tune models, evaluate them in the real world, analyze results, and repeat. This role helps make that loop faster, more rigorous, and more reliable.

You will work closely with ML researchers and robotics engineers to run robot experiments, design evaluation tasks, brainstorm ideas, collect data, interpret results, and document repeatable workflows.

A major part of this role is helping ensure our evaluations are trustworthy. We care deeply about experimental design, controls, hands-on iteration, sample sizes, variance, repeatability, and statistical rigor.

You’ll be responsible for:

• Running structured experiments on robot platforms

• Setting up physical tasks, materials, fixtures, and benchmarks for robot evaluations

• Collecting high-quality robot data and tracking experimental conditions

• Measuring real-world success rates across tasks, robots, and model variants

• Designing evaluations with attention to controls, repeatability, statistical rigor, and sources of bias

• Analyzing results to help distinguish real model improvements from noise

• Synthesizing findings and communicating them clearly to ML researchers and engineers

• Preparing robots, sensors, workspaces, and materials for rollouts and evaluations

• Helping kick off training jobs, run evaluations, and organize results

• Beta testing internal and third-party tools for teaching robots new skills

• Troubleshooting physical setups, hardware issues, and procedural bottlenecks

• Writing clear documentation and playbooks so others can reproduce workflows

• Improving experimental reliability, data quality, and operational throughput over time

You might thrive in this role if you:

• Have experience running experiments, lab studies, field studies, data collection workflows, or structured evaluations

• Think carefully about experimental design, confounding factors, controls, sample sizes, variance, and what conclusions the data can actually support

• Are diligent and detail-oriented, especially when tasks are repetitive but subtle differences matter

• Enjoy hands-on work with physical systems, equipment, materials, or instruments

• Are comfortable following protocols while also noticing when something is wrong or could be improved

• Can coordinate many moving parts: robots, materials, tasks, data, model versions, metrics, and documentation

• Communicate clearly and can summarize what happened, what changed, and what the evidence suggests

• Are curious about machine learning and robotics, even if you are not yet an expert in either

• Have some exposure to programming, data analysis, robotics, hardware, electronics, mechanical assembly, or experimental tooling

• Prefer fast iteration, careful measurement, and empirical progress over abstract theory alone