Clera
Research Engineer / Research Scientist, Robotics and Physical AI
About this role
Join an early-stage AI training data company as a Research Engineer to advance physical AI and robotics. You'll develop learning systems, build simulation environments, and work directly with robot hardware to bridge the gap between simulation and real-world deployment.
What you'll do
- Develop robot learning systems including policies, world models, and vision-language-action models
- Build and maintain simulation environments and evaluation frameworks for robotic tasks
- Apply computer vision and perception algorithms for autonomous robot systems
- Create pipelines connecting simulation to real-world deployment with sim-to-real workflows
- Work directly with robot hardware, sensors, actuators, and embedded systems to prototype and validate
- Train and evaluate reinforcement-learning and imitation-learning systems for manipulation and locomotion
What they're looking for
- Python and C++
- Deep learning and reinforcement learning
- Computer vision and perception algorithms
- Simulation platforms (Isaac Lab, MuJoCo, MJX, Genesis)
- Robotics hardware and embedded systems
- Sim-to-real and real-to-sim workflows
- Vision-language-action models and transformers
- 3D modeling and CAD tools
Benefits
- Work on cutting-edge physical AI and robotics research
- Full-stack ownership from algorithms to real-world deployment
- Collaborate with external robotics companies and research labs
- On-site team environment in San Francisco
- Competitive salary commensurate with experience
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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
- Walk us through a robotics project where you bridged simulation and real-world deployment—what challenges did you encounter?
- Describe your experience training reinforcement learning or imitation learning systems. How did you handle sim-to-real transfer?