About the role
You will advance the methods at the heart of Algebra — certified safety for learned, high-dimensional robot policies — and turn them into something that ships. The role bridges deep research and real deployments: you will prototype, validate on real systems, and publish.
What you will do
- Develop methods for certifying the safety of learned and classical policies
- Prototype and benchmark on simulated and physical robots
- Translate research advances into the production safety layer
- Publish and represent the work in the research community
What we are looking for
- A PhD, or an equivalent research track record, in controls, robotics, machine learning, or a related field
- A background in safe control, reinforcement learning, optimization, or verification
- Strong implementation skills — you build, not only derive
Nice to have
- Publications in safe control or robot learning
- Experience taking research from paper to a deployed system
Compensation & benefits
- $140,000 – $180,000 base salary, depending on experience
- Meaningful founding equity
- Medical, dental, and vision coverage
- Flexible paid time off
Algebra