Maven Robotics | Machine Learning Engineer

Role Description

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.

Qualifications

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.

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