Applied Scientist

Generalist; San Francisco (CA); Hybrid; USD 180,000 - 280,000; Full time

Job summary

Generalist in San Francisco is seeking Applied Scientists to advance dexterous task capabilities for embodied foundation models. You will own end-to-end improvement of target capabilities, design evaluation protocols, coordinate data collection, and run large-scale experiments to validate models across real-world tasks.

This empirical, model-science-first role blends research, engineering, and operations, requiring hands-on experimentation with robot setups and clear communication to research

Qualifications

• Strong background in machine learning and robot learning.
• Experience designing and running large-scale experiments.
• Ability to analyze experimental results and communicate findings.

Responsibilities

• Own end-to-end improvement of target capabilities from baseline to mastery.
• Design task suites and evaluation protocols that generalize beyond a single deployment.
• Coordinate data collection, training/fine-tuning of models, and large-scale evaluations.
• Run rigorous experimental analysis across model variants and data recipes; diagnose failures.

Skills

Machine Learning

Robot Learning

Experimentation

Data Analysis

Education

PhD in a relevant area

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

Applied Scientists own the mastery of new frontier model capabilities. This includes developing post-training recipes for learning a wide range of dexterous tasks — from pushing the frontiers of science lab automation, to generalizing manufacturing and warehouse logistics, to designing open-ended evaluations that challenge what the models can do, and more. You are responsible for hill-climbing broad sets of manipulation capabilities until they are robust, general, and reusable across multiple applications. This role is a hybrid of research, engineering, and operations, and we expect excellence in all three areas.

This is an empirical, model science-first role. You will define what \"good\" looks like for a capability, build the evaluations that measure it, run experiments across model variants and data recipes, analyze the results, and drive the post-training loop. You will leverage post-training, data, task design, and rigorous experimentation, to compound model capabilities: anything mastered for one application can become a strength of the foundation model for entire industries.

You’ll be responsible for:

• Owning end-to-end improvement of target capabilities, from baseline measurement to demonstrated mastery


• Designing task suites and evaluation protocols that faithfully capture what research, science partners, and customers need, and that generalize beyond any single deployment


• Coordinating data collection, training and fine-tuning models, sweeping data mixtures and configurations, and orchestrating large-scale evaluations


• Running rigorous experimental analysis across model variants — isolating variables, quantifying statistical confidence, and diagnosing failure modes from rollout data

You might thrive in this role if you:

• Are comfortable with data analysis at scale: slicing roll out logs, computing metrics, visualizing results, and investigating what happened and why


• Care deeply about evaluation, benchmarking, and failure analysis — and are continuously diligent in the face of seemingly repetitive, but subtly changing task evaluations


• Enjoy rapid iteration with real-world feedback loops, and are willing to get hands‑on with robots and physical task setups when the experiment demands it


• Are motivated by making capabilities work for real applications


• Can communicate findings clearly to both research teams and partner-facing teams


• Have a strong background in machine learning and/or robot learning (e.g. a PhD in a relevant area)