The RoleWe're hiring a researcher to join Trace and work on the core problems in robot learning: what data quality means in the paradigm of robot learning. You'll work on how to structure real-world data for training, how to train and evaluate models on it, and how to close the loop between what we capture and what makes physical AI systems better.
This role reports to our Chief Scientist. You'll be expected to do frontier research on data quality, running experiments that will push forward our understanding of how data moves model performance. We're looking for people motivated by seeing their research move a system forward, with strong technical opinions, weakly held.
What You'll Do:- Design and run experiments on training robotics, vision, and embodied AI models using Trace's real-world data.
- Investigate how data quality, structure, and diversity affect downstream model performance, and turn those findings into concrete recommendations for what we capture.
- Build and iterate on evaluation methodology so we know whether a model, or a dataset, is actually getting better.
- Stay close to the literature on robot learning and translate what's relevant into work we can actually ship.
- Work directly with the engineering and data teams so research findings turn into pipeline and product changes.
Who You Are:- Currently completing, or recently completed, a Master's or PhD program with a research focus on robot learning, robotics, or training foundation/AI models.
- Exceptional undergraduates from top-tier robotics programs will also be considered.
- No industry experience required. You can join us straight out of your program.
- If you're coming from industry rather than a research program, your background should be a researcher role at a robotics company or an equally reputable research lab. General industry engineering experience without a research track record isn't a fit for this role.
- Motivated by seeing research translate into working systems
- Optimistic and serious about where robotics and AI are headed.
- Comfortable being early in your career but still forming strong, defensible technical opinions.
Bonus:- Publications or research work specifically in robot learning or training models for physical/embodied systems
- Experience from a leading robotics research program or lab
- Hands-on exposure to real robotic hardware or real-world (versus purely simulated) data
