Clera
Research Engineer / Research Scientist, Robotics and Physical AI
About this role
Join an early-stage robotics AI company to develop learning systems and simulation frameworks that bridge the gap between simulated robot training and real-world deployment. You'll own end-to-end projects spanning algorithms, computer vision, hardware integration, and physical robot testing.
What you'll do
- Develop robot learning policies, world models, and vision-language-action systems
- Build and maintain simulation environments and evaluation frameworks for robotic tasks
- Apply computer vision and perception algorithms for autonomous robot capabilities
- Create sim-to-real and real-to-sim pipelines connecting simulation to physical deployment
- Work with robot hardware, sensors, actuators, and embedded systems for prototyping
- Train and evaluate reinforcement learning and imitation learning systems for manipulation or locomotion
What they're looking for
- Python and C++
- Deep learning and reinforcement learning
- Computer vision
- Simulation platforms (Isaac Lab, MuJoCo, MJX, Genesis)
- Sim-to-real transfer and domain adaptation
- Robotics hardware and embedded systems
- CAD and mechanical engineering
- Multimodal AI (transformers, diffusion models, world models)
Benefits
- Competitive salary range $100K–$300K annually
- On-site position in San Francisco with modern robotics infrastructure
- End-to-end ownership and autonomy as an individual contributor
- Collaboration with leading robotics companies and research labs
- Work on cutting-edge physical AI and embodied intelligence
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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 sim-to-real or real-to-sim project you've led—what were the key challenges and how did you solve domain gap issues?
- Describe your hands-on experience with robot hardware and sensors. What platforms have you worked with and what mechanical problems did you troubleshoot?