About this role
Mundane is a venture-backed seed-stage robot learning startup founded by a team of Stanford researchers and builders. They are seeking a Robot Learning Research Engineer to develop and ship learning-based manipulation policies that run on real robots, focusing on improving reliability and generalization across tasks. The role involves hands-on implementation, running experiments, and validating improvements directly on physical robots.
Responsibilities:
- Extend and improve our policy learning stack (imitation learning / sequence-based policies) for real-world manipulation tasks
- Design and run disciplined experiments to improve policy performance, including clear ablations and controlled comparisons
- Develop multitask policies with effective task conditioning and thoughtful data mixture strategies
- Improve robustness through techniques such as data augmentation, recovery behaviors, and training under partial observability
- Design and run systematic stress tests to evaluate distribution shift, drift, and edge-case failures
- Work closely with infrastructure engineers to scale training pipelines and experiment workflows
- Collaborate with reliability engineers to define evaluation gates and deployment criteria
- Package trained models for deployment, addressing latency, stability, and safety constraints
- Investigate real-world failures and iterate rapidly to improve policy robustness