Machine Learning Research Engineer/Scientist

About the role

This role develops software and algorithms that enable dexterous, safe manipulation for home robots. The position operates inside a small, cross-functional team that owns the full robotics stack from data to deployed behavior.

Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.

Key facts

What you'll do

This advances embodied AI for mobile manipulation that is safe around people.

The data system and fleet feed continuous learning for varied tasks.

This closes the cycle from experiment to behavior in consumer robots.

Joint effort aligns software and hardware for real home use.

Clear, code keeps the system reliable as it evolves.

Requirements

The posting states a bachelor's degree requirement. Bring 3+ years of experience in machine learning for robotics, controls, or perception to ensure sufficient depth for algorithm design.

Use Python and a deep learning framework such as PyTorch to implement and iterate on complex models.

Apply hands-on experience with robot learning for real-world tasks, including both offline and online training and evaluation.

Build tooling for visualization, debugging, and evaluation of data and learning pipelines.

Communicate and collaborate effectively inside fast-moving, cross-functional teams so products and technology move together.

Nice to have

Understand classical robotics fundamentals such as controls, planning, and state estimation to strengthen system design.

Feel comfortable profiling and optimizing for latency, memory, and compute on edge devices.

Have experience with large-scale model training pipelines for LLM, VLM, and VLA approaches.

Practical notes

The position is based in Redwood City, operates full-time, and falls under equal opportunity principles. The team values diverse, curious, creative people.

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

Machine learning research in robotics focuses on perception, control, and learning-based planning. Real-world data collection systems and robot fleets are central to scaling behavior. Production quality code and tooling are essential for reliable deployment and iteration.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.