ML Research Scientist, Robot Learning

Weave was founded to build the robots we’d want to have in our own home. We believe the next generation of robotics will transform everyday life by enabling people to do more and to reclaim time to spend on what’s important.

We also believe robots are in a sense like any other product: to matter, they have to ship. Our robots are already operating in real homes and businesses, giving us the opportunity to rapidly improve from real-world experience. With a growing team, strong customer demand, and capital for expansion, we’re entering an exciting stage of growth—and we’re looking for people with exceptional talent and standards to help bring home robotics to millions of households.

Most robot learning research is graded on evals that don't survive contact with the field. Ours is graded by robots doing useful work in real homes and businesses, every day. It's an eval that can't be gamed, and you'll have it in a weekly loop.

We're looking for a researcher who's fluent in the current paradigm (VLAs, world models, large-scale post-training) and has their own view of what comes after it. You form sharp hypotheses about what will scale, what will generalize across environments, tasks, and robots, and you test them against reality. Your research doesn't inform the roadmap; it sets it.

• Chart research direction: Work with the research team to establish a long term roadmap for the company.

• Own a research direction end to end: hypothesis, data, training, deployment.

• Architectural experimentation: WAMs, VLAs, uncharted territory. You will be expected to push past the frontier, not just slightly over-do SOTA.

• Help architect the data roadmap: Expert demonstrations, egocentric data, online and offline reinforcement learning. Work with other researchers and the Data team to chart out the roadmap.

• Evaluation: deploy trained models onto robots, come up with good metrics, and use real-world performance to continuously refine robot behavior.

• Write great software: Build maintainable, scalable machine learning infrastructure and research code that supports both experimentation and production deployment.

• Research track record: Demonstrated ability to conduct independent research and develop novel machine learning approaches, whether through industry experience, publications, or equivalent contributions.

• Deep machine learning expertise and intuition: Deep experience in AI and robotics research, specifically in deploying deep learning models onto physical robots. Experience with the state of the art in robot learning is essential.

• Hands-on, rapid experimentation: Ability to quickly turn multiple research ideas into well-designed experiments, interpret results, and iterate based on evidence.

• ML tooling: Strong experience with deep learning frameworks (one of PyTorch, JAX).

• Strong software engineering skills: Expert proficiency in Python, adept at interfacing with complex infrastructure, and being comfortable debugging the full software stack.

• Post-training: Experience with large-scale post-training workflows, including supervised fine-tuning, reward modeling, and alignment techniques.

• ML tooling mastery: Hands-on experience with distributed training, cloud infrastructure (GCP/AWS), and cluster management (Kubernetes, SLURM, or similar).