Founding Robotics Engineer

Reflection Robotics; Seattle (WA); On-site; USD 180,000 - 240,000; Full time

Job summary

Reflection Robotics is seeking a founding team member to shape the future of our robotics platform. You’ll work across research, engineering, and production, turning data into real-time control systems and production-ready solutions on customer sites.

You will balance learning-based approaches with solid engineering, applying data-driven methods when they make sense and inventing new tech when needed, all while collaborating closely with a small, hands-on team in Seattle.

Qualifications

• Strong software engineering fundamentals.
• Experience training and deploying ML models (robotics, computer vision, or similar).
• Comfort debugging real systems, hardware included, not just code in isolation.
• Ambition to tackle challenging, open-ended problems in a fast-paced environment.
• Ability to work in-person in Seattle on weekdays.

Responsibilities

• Train and iterate on robot foundation models, from data collection through deployment
• Optimize models for robot throughput, inference speed, and reliability
• Debug failures directly on hardware by going beyond logs and metrics to understand what’s happening physically
• Decide when a problem needs a learned solution versus a classical engineering one, and build the latter when it’s the better call
• Generate insights that actually change what the team builds next
• Work directly with customers’ real manufacturing environments and constraints
• Collaborate closely with a small team where everyone touches research, infra, and hardware

Skills

Software engineering fundamentals

ML models training & deployment

Hardware debugging

Open-ended problem solving

Seattle in-person work

Job description

The Role

As a founding team member, you’ll have outsized influence over how our technology, product, and team take shape from here. At Reflection Robotics, we don’t separate “research” from “engineering”, because robotics doesn’t work that way. A model that performs beautifully in simulation and fails on the robot hasn’t solved anything. We need people who can move fluidly across the entire stack: training models, optimizing them to run fast enough for real-time control, debugging why something breaks on hardware, and coming up with insights to improve the performance of the system both on the model side and control side of things.

Just as important: we don’t assume learning-based approaches are always the answer. Sometimes the right solution is a well-engineered controller, a better sensor pipeline, or a cleaner mechanical fix instead of a bigger model. We’re looking for people who pick the right tool for the problem, not people wedded to one method.

At the same time, some problems can’t be solved with better engineering alone — they require inventing new technology. When that’s the case, we fully embrace data-driven, learning-based approaches to get there. The goal isn’t to avoid learning systems or default to them; it’s knowing which problems call for invention and which call for solid engineering, and being equally capable of both.

You’ll also spend real time on production work for customers. This isn’t a pure research role insulated from deadlines and real-world constraints. Expect to wear a lot of hats.

What You’ll Do

• Train and iterate on robot foundation models, from data collection through deployment

• Optimize models for robot throughput, inference speed, and reliability

• Debug failures directly on hardware by going beyond logs and metrics to understand what’s actually happening physically

• Decide when a problem needs a learned solution versus a classical engineering one, and build the latter when it’s the better call

• Generate insights that actually change what the team builds next

• Work directly with customers’ real manufacturing environments and constraints

• Collaborate closely with a small team where everyone touches research, infra, and hardware

What We’re Looking For

Must-haves:

• Ambition to tackle challenging, open-ended problems in a fast-paced environment, and willingness to wear many hats

• Strong software engineering fundamentals

• Experience training and deploying ML models (robotics, computer vision, or similar)

• Comfort in debugging real systems, hardware included, not just code in isolation

• A track record of solving problems pragmatically

• Ability to work in-person in Seattle on weekdays

Nice-to-haves:

• Experience with robot learning, manipulation, or foundation models

• Experience in RL, RLHF, or continuous learning on real robots

• Experience in building and deploying production-grade robotics systems in factories

• Background in large scale training of vision models, VLMs, or LLMs

• Experience deploying models under real-time or latency constraints

• Experience working directly with manufacturing customers or environments

We care much more about how you think and what you’ve built than a specific degree or years-of-experience number. If you’ve done work that maps to this but doesn’t check every box above, we’d still like to hear from you.