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Clera

ML Engineer – Robotics

San Francisco$220k–$300kfulltimemidAdded today

About this role

A well-funded AI/ML platform company seeks an experienced ML Engineer to develop and deploy intelligent models for autonomous robotics systems. You'll work across perception, control, and planning, integrating learning-based approaches with real-world robotic hardware and ROS software stacks.

What you'll do

  • Develop and optimize ML models for perception, motion planning, and robotic control systems
  • Build computer vision and sensor fusion pipelines using 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 models in live robotic environments for robustness and performance
  • Collaborate with hardware and robotics engineers on cross-functional integration

What they're looking for

  • Python and C++ for robotics development
  • ROS/ROS2 software integration
  • Sensor fusion and multi-sensor perception (camera, LiDAR, IMU)
  • Simulation environments (Gazebo, Isaac Sim, CARLA, MuJoCo, PyBullet)
  • Reinforcement learning and imitation learning
  • Computer vision and motion planning
  • Model training and deployment in production robotics systems
  • Cross-functional collaboration and technical communication

Benefits

  • Salary: $220,000–$300,000 USD annually
  • Competitive equity package
  • Work on cutting-edge embodied AI and robotics problems
  • Collaborate with leading researchers and engineers
  • On-site access to robotics hardware and infrastructure
  • Well-funded company with resources for ambitious projects
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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.

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Likely interview questions

  • Walk us through a robotics project where you deployed a learning-based model in a live system—what were the biggest challenges?
  • How have you approached sensor fusion when working with multiple sensor modalities (camera, LiDAR, IMU)?
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