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Clera

ML Engineer - Robotics

remote (Remote)$220k–$300kfulltimemidAdded yesterday

About this role

A Series A AI company is seeking an ML Engineer – Robotics to design and deploy intelligent models for autonomous systems, combining perception, planning, and control. You'll build computer vision and sensor fusion pipelines, integrate learning-based systems with robotics stacks (ROS/ROS2), and collaborate with hardware teams to deploy models in real-world environments.

What you'll do

  • Develop and optimize ML models for perception, motion planning, and control in autonomous 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 workflows
  • Collaborate with robotics and hardware engineers to deploy and validate models in live environments
  • Evaluate model performance and robustness across diverse real-world scenarios

What they're looking for

  • Python and C++ for robotics and ML development
  • PyTorch and/or TensorFlow
  • ROS/ROS2
  • Robotics simulation tools (Gazebo, Isaac Sim, CARLA, MuJoCo, PyBullet)
  • Sensor fusion and computer vision
  • Reinforcement learning and imitation learning
  • SLAM and localization
  • Motion planning and control systems

Benefits

  • Competitive salary of $220,000–$300,000 USD annually
  • Work on cutting-edge AI and robotics systems
  • Collaboration with frontier AI labs and enterprises
  • Remote work flexibility with on-site location in Mountain View, CA
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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

  • Describe your experience integrating machine learning models into ROS/ROS2 stacks and any challenges you encountered.
  • Tell us about a time you deployed a perception or planning model in a real-world robotics system. How did you handle the sim-to-real gap?
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