Applied Scientist

Generalist • Somerville, MA

1d • Hybrid

About The Position

Applied Scientists own the mastery of new frontier model capabilities by developing post-training recipes that enable a wide range of dexterous tasks. These tasks may include advancing science lab automation, generalizing manufacturing and warehouse logistics, and designing open-ended evaluations. The role is responsible for improving broad manipulation capabilities until they are robust, general, and reusable across applications.

This is an empirical, model-science-first role that combines research, engineering, and operations. The Applied Scientist defines capability goals, builds evaluations, runs experiments across model variants and data recipes, analyzes results, and drives the post-training loop. The work leverages post-training, data, task design, and rigorous experimentation to build model capabilities that can transfer across applications and industries.

Requirements

• Strong background in machine learning and/or robot learning.
• Comfort with large-scale data analysis, including slicing rollout logs, computing metrics, visualizing results, and investigating outcomes.
• Care about evaluation, benchmarking, and failure analysis, with diligence across repetitive but subtly changing task evaluations.
• Enjoy rapid iteration with real-world feedback loops.
• Motivation to make capabilities work for real applications.
• Ability to communicate findings clearly to research teams and partner-facing teams.

Nice To Haves

• A PhD in a relevant area.

Responsibilities

• Own end-to-end improvement of target capabilities, from baseline performance to 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, model training and fine-tuning, sweeps of data mixtures and configurations, and large-scale evaluations.
• Run rigorous experimental analysis across model variants, isolating variables, quantifying statistical confidence, and diagnosing failure modes from rollout data.
• Use the existing stack in new ways; collaborate with pretraining and post-training teams to improve the model when a capability is fundamentally restricted.
• Analyze rollout logs, compute metrics, visualize results, and investigate outcomes and their causes.
• Communicate findings clearly to research teams and partner-facing teams.
• Get hands-on with robots and physical task setups when experiments require it.

What This Job Offers

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