Clera
Research Engineer / Research Scientist — Robotics & Physical AI
About this role
Join a small, high-impact robotics team building AI systems that enable robots to learn and operate in the physical world. You'll bridge research and engineering across learning algorithms, simulation, computer vision, and hardware to deploy robots from simulation to real-world environments.
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 operation
- Create sim-to-real and real-to-sim pipelines for robot deployment
- Work with robot hardware, sensors, actuators, and embedded systems for prototyping
- 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
- Robotics simulation (Isaac Lab, MuJoCo, MJX, Genesis)
- Sim-to-real and real-to-sim workflows
- Robotics hardware and embedded systems
- CAD and Blender
- Modern ML tools (LLMs, Transformers, diffusion models)
Benefits
- Work on cutting-edge physical AI and robotics research
- Small, high-output team environment
- On-site collaboration in San Francisco
- Hands-on experience across research, engineering, and product development
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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 moved from simulation to real-world deployment—what were the main challenges?
- Describe your experience with reinforcement learning or imitation learning for robot control. What frameworks did you use?