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We are looking to recruit an exceptional Machine Learning Engineer - Robot Perception to design, implement, test, and deploy robot perception algorithms that power our robots’ ability to understand and interact with the world.

In this role you will:

• Develop, train, and deploy ML-based perception algorithms for object detection, pose estimation, tracking, and scene understanding.
• Integrate sensor fusion techniques using cameras, depth sensors, IMUs, and tactile feedback.
• Optimize real-time perception pipelines for low-latency and robust performance in dynamic environments.
• Work closely with hardware engineers to design sensor configurations and optimize perception models for onboard deployment.
• Contribute to our broader AI and autonomy stack, ensuring seamless integration with reasoning, manipulation, planning and control.
• Collaborate across disciplines to ensure seamless integration of ML models and provide technical mentorship to junior engineers.

Must-have:

• MS or PhD in machine learning, computer science, robotics, or a related field.
• Strong background in computer vision, deep learning, and sensor fusion.
• Proficiency in Python and C++, with experience in frameworks like PyTorch, TensorFlow, OpenCV, and ROS.
• Hands-on experience with real-world robotics perception systems (e.g., SLAM, 3D reconstruction, multimodal perception).
• Experience working with hardware, including setting up and calibrating cameras, LiDAR, and other sensors.
• Experience with data collection, preprocessing, and management in the context of training ML models.
• Self-starter attitude with strong ability to identify problems, prioritize them, then plan and execute working solutions.
• Enthusiasm for working in a fast paced startup environment and eagerness to support the team on a variety of topics.

Nice-to-have:

• Familiarity with robotic simulation environments (e.g., Gazebo, MuJoCo) and experience in sim-to-real transfer.
• Experience in:
  • Developing models that can handle noisy, incomplete, or sparse data.
  • Deployment of ML models to edge devices for real-time inference (e.g., NVIDIA Jetson).
  • Accelerating ML training processes using GPU, TPU, or other HW accelerators.
  • General knowledge of robotics principles, including kinematics, dynamics, and control.