Maven Robotics

Machine Learning Engineer - Robot Perception

San Francisco Bay Area, California USA · Junior

Sponsorship not specifiedDetected 169 days ago

PythonC++AlgorithmsMachine LearningDeep LearningTensorFlowPyTorchComputer VisionRoboticsSensorsMentoring

About the role

Maven Robotics is building the world's leading general-purpose AI robots.
We are currently operating in stealth and are growing the world's best team in AI robotics. We are looking for self-starters that are the world's best in their field, who can innovate from a deep understanding of the fundamentals, and who share our values of unwavering truth seeking and integrity, humility, curiosity, and relentless determination.

Develop, train, and deploy ML-based perception algorithms for object detection, pose estimation, tracking, and scene understanding.
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.
Collaborate across disciplines to ensure seamless integration of ML models and provide technical mentorship to junior engineers.

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.
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.
Enthusiasm for working in a fast paced startup environment and eagerness to support the team on a variety of topics.
Nice-to-have:
Experience in:
Developing models that can handle noisy, incomplete, or sparse data.

Familiarity with robotic simulation environments (e.g., Gazebo, MuJoCo) and experience in sim-to-real transfer.
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.

This listing is sourced directly from Maven Robotics's careers page and normalized into a canonical job model.