Applied Scientist

Generalist

1d

About The Position

Applied Scientists at Generalist develop frontier model capabilities, including post-training recipes for learning a broad range of dexterous tasks. These tasks may span science lab automation, manufacturing, warehouse logistics, and open-ended evaluations. The role owns the improvement of manipulation capabilities until they are robust, general, and reusable across applications. It combines research, engineering, and operations, with an empirical, model-science-first approach.

The position defines success for a capability, builds evaluations to measure it, experiments across model variants and data recipes, analyzes results, and drives the post-training loop. It uses post-training, data, task design, and rigorous experimentation to build capabilities that can transfer across applications and industries.

Requirements

• Strong background in machine learning and/or robot learning; a PhD in a relevant area is given as an example.
• Comfort with large-scale data analysis, including slicing rollout logs, computing metrics, visualizing results, and investigating outcomes.
• Care about evaluation, benchmarking, and failure analysis, and maintain diligence across repetitive but subtly changing task evaluations.
• Enjoy rapid iteration with real-world feedback and be willing to work hands-on with robots and physical task setups when needed.
• Be motivated by making capabilities work for real applications.
• Communicate findings clearly to research teams and partner-facing teams.

Responsibilities

• Own end-to-end improvement of target capabilities, from baseline measurement to demonstrated mastery.
• Design task suites and evaluation protocols that capture the needs of research, science partners, and customers and generalize beyond a single deployment.
• Coordinate data collection, train and fine-tune models, sweep data mixtures and configurations, and orchestrate large-scale evaluations.
• Conduct rigorous experimental analysis across model variants, including isolating variables, quantifying statistical confidence, and diagnosing failure modes from rollout data.

What This Job Offers

• Job Type: Full-time
• Education Level: No Education Listed