Robotics ML Research Assistant

Generalist; Medford Hillside (MA); On-site; USD 60,000 - 85,000; Full time

Job summary

Generalist is seeking a Research Assistant to help design, run, and analyze experiments at the intersection of machine learning and robotics. This entry‑level role is designed as a path toward Research Scientist, with opportunities to contribute to rigorous evaluation and reproducible workflows.

You'll work closely with ML researchers and engineers to run robot experiments, collect data, and document results, helping improve experimental reliability and throughput over time.

Responsibilities

• 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

Job description

Generalist is seeking a Research Assistant to help design, run, and analyze experiments at the intersection of machine learning and robotics. This entry‑level role is designed as a path toward Research Scientist, with opportunities to contribute to rigorous evaluation and reproducible workflows.

You'll work closely with ML researchers and engineers to run robot experiments, collect data, and document results, helping improve experimental reliability and throughput over time.