Role DescriptionWe 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.
QualificationsMust-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.
