Clera
ML Engineer - Robotics
About this role
Build machine learning systems for autonomous robots that perceive, plan, and make decisions in the real world. You'll develop perception pipelines, integrate learning models with robotics software, and collaborate with AI researchers and hardware engineers to deploy adaptive systems at scale.
What you'll do
- Develop and optimize ML models for perception, motion planning, and control in autonomous systems
- Build computer vision and sensor fusion systems integrating camera, LiDAR, and IMU data
- Integrate learning-based models with ROS/ROS2 robotics software stacks
- Design data collection, simulation, and reinforcement learning pipelines
- Deploy and evaluate ML models in live robotic environments for real-world performance
- Collaborate with robotics and hardware engineers to solve interdisciplinary technical challenges
What they're looking for
- Machine learning model development (PyTorch, TensorFlow)
- Python and C++ programming for robotics applications
- ROS/ROS2 integration and deployment
- Computer vision and sensor fusion
- Simulation environments (Gazebo, Isaac Sim, CARLA, MuJoCo, PyBullet)
- Reinforcement learning and imitation learning
- Motion planning and control systems
- Real-time and embedded ML deployment
Benefits
- Competitive base salary $220,000–$300,000 annually
- Work on cutting-edge embodied AI and robotics systems
- Collaborate with frontier AI researchers
- On-site in Mountain View with access to Silicon Valley resources
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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
- Describe a complex perception pipeline you built for a robotics system—what sensors did you fuse and how did you handle real-world noise?
- Walk us through your experience deploying ML models in embedded or real-time robotic environments and the optimizations you made.