Clera
ML Engineer – Robotics
About this role
Build intelligent autonomous systems at the intersection of machine learning and robotics. Design, train, and deploy perception, planning, and control models for real-world embodied AI applications, working with cutting-edge simulation and sensor fusion technologies.
What you'll do
- Develop and optimize ML models for perception, motion planning, and robot control systems
- Build multi-sensor perception pipelines 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 validate models in live robotic environments with hardware teams
- Evaluate model robustness and performance across diverse real-world scenarios
What they're looking for
- Python and C++ for robotics development
- ROS/ROS2 software integration
- Computer vision and multi-sensor perception
- Simulation environments (Gazebo, Isaac Sim, CARLA, MuJoCo, PyBullet)
- Machine learning model design and deployment
- Reinforcement learning and imitation learning
- PyTorch and/or TensorFlow
- SLAM, localization, or control systems
Benefits
- Competitive salary $220,000–$300,000 annually
- Equity compensation
- Work on frontier AI and embodied intelligence problems
- Collaborative cross-functional environment
- Hands-on experience with cutting-edge robotics systems
- Bay Area location with on-site collaboration
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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 project where you integrated a trained ML model into a real-time robotics system—what were the key challenges?
- How have you approached multi-sensor fusion in past projects, and how did you handle sensor misalignment or dropout?