About the Role:

We are looking for a hands-on Research Assistant to help run real-world experiments at the intersection of robotics, machine learning, and physical-world evaluation.

At Generalist, we are building foundation models for robots. These models improve through a tight feedback loop: 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, collect data, measure success rates, and document repeatable workflows. You do not need to be an experienced ML research scientist or robotics engineer, but you should be excited by careful experimentation, physical systems, statistical rigor, and hands-on iteration.

A major part of this role is helping ensure our evaluations are trustworthy. We care deeply about experimental design, controls, sample sizes, variance, repeatability, and avoiding misleading conclusions from noisy real-world robot trials.

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 power, 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, statistical rigor, and empirical progress over abstract theory alone

What This Role Is Not

You will be part of the ML team and work closely with ML researchers, including contributing to experiment design, interpreting results, brainstorming ideas, and occasionally prototyping. This is not a pure ML Research Scientist role at the outset; you will not be expected to start by designing new model architectures or advancing core learning algorithms.

That said, this role is designed to be a potential path into research. For someone with strong scientific judgment, technical curiosity, and excellent execution, it can grow into deeper research ownership over time, including proposing experiments, shaping evaluation methodology, and eventually contributing as a Research Scientist.

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.