Robotics ML Research Scientist - Real-World Learning

Weave Robotics, Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 150,000 - 230,000; Full time

Job summary

Weave Robotics, Inc. is seeking a researcher fluent in modern robotics learning paradigms to help set the direction for real-world deployments.

You will frame sharp hypotheses about scale and generalization, test them on hardware, and translate findings into production-ready software. Responsibilities include charting research direction, owning end-to-end projects, leading architectural experimentation, shaping the data roadmap, evaluating models on robots, and building maintainable ML

Qualifications

• Independent research track record demonstrated via industry experience, publications, or equivalent.
• Deep experience in AI and robotics research, especially deploying models on physical robots.
• Ability to quickly turn ideas into well-designed experiments and iterate based on results.
• Strong software engineering skills in Python and interfacing with complex infra.

Responsibilities

• Chart research direction: establish a long-term roadmap for the company.
• Own a research direction end to end: hypothesis, data, training, deployment.
• Architectural experimentation: WAMs, VLAs, pushing beyond SOTA.
• Help architect the data roadmap: expert demonstrations, egocentric data, online/offline RL.
• Evaluation: deploy models on robots, define metrics, refine behavior.
• Write great software: maintainable ML infra for experimentation and production.

Skills

Research track record

Deep ML expertise

Rapid experimentation

Python software engineering

Communicate across teams

Tools

PyTorch

JAX

Job description

Weave Robotics, Inc. is seeking a researcher fluent in modern robotics learning paradigms to help set the direction for real-world deployments.

You will frame sharp hypotheses about scale and generalization, test them on hardware, and translate findings into production-ready software. Responsibilities include charting research direction, owning end-to-end projects, leading architectural experimentation, shaping the data roadmap, evaluating models on robots, and building maintainable ML