Clera
Research Engineer / Research Scientist — Robotics & Physical AI
About this role
Join a Y Combinator-backed Physical AI startup to develop learning systems and perception algorithms for robots that operate in the real world. You'll work across the full stack—from deep learning and simulation to hardware integration—collaborating with leading robotics companies to ship research into actual deployments.
What you'll do
- Develop robot learning systems including policies, world models, and vision-language-action models
- Build simulation environments and evaluation frameworks for robotic tasks
- Apply computer vision and perception algorithms for autonomous robot systems
- Create sim-to-real and real-to-sim pipelines to bridge simulation and physical deployment
- Work hands-on with robot hardware, sensors, actuators, and embedded systems to validate algorithms
- Train and evaluate reinforcement learning and imitation learning systems for manipulation and locomotion
What they're looking for
- Python and C++
- Robotics hardware and embedded systems
- Deep learning and reinforcement learning
- Computer vision
- Sim-to-real and real-to-sim workflows
- Embodied AI and vision-language-action models
- Simulation platforms (Isaac Lab, MuJoCo, Gazebo, or PyBullet)
- ML methods (Transformers, multimodal AI, diffusion models)
Benefits
- Early-stage equity at a seed-stage Y Combinator-backed company
- Work on cutting-edge Physical AI research that ships to real deployments
- Hands-on experience across the full robotics stack
- Collaboration with leading robotics companies and research labs
- Opportunity to work in a small, fast-moving team
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Clera
Clera builds an agentic operating system that automates complex workflows and processes through AI agents, with a platform designed to simplify distributed infrastructure management for developers. The company is hiring Founding Engineers, Customer Engineers, and Product Engineers to develop both backend systems and user-facing interfaces across their AI automation products.
View all jobs at CleraLikely interview questions
- Can you describe a robotics or embodied AI project where you worked on both simulation and real-world hardware—what were the biggest challenges in closing the sim-to-real gap?
- Walk us through your experience training reinforcement learning or imitation learning systems for robot control. How did you approach data collection or policy evaluation?