Robotics Learning Engineer

Noble Machines, Inc•Sunnyvale, CA

1d•$170,000 - $400,000

About The Position

Noble Machines (formerly Under Control Robotics) builds multipurpose robots to support human workers in the world's toughest jobs—turning dangerous work from a necessity into a choice. Our work demands reliability, robustness, and readiness for the unexpected—on time, every time. We're assembling a mission-driven team focused on delivering real impact in heavy industry, from construction and mining to energy. If you're driven to build rugged, reliable products that solve real-world problems, we'd love to talk. Position Overview At Noble Machines, building is a team sport. As a machine learning engineer, you'll take ownership and lead the development of advanced machine learning and AI systems powering multipurpose humanoid robots in the real world. You'll design, implement, and optimize learning algorithms that enable robots to move fluidly across diverse environments while performing complex manipulation tasks.

Requirements

Master's or PhD in Robotics, Computer Science, Machine Learning, or related field
3+ years of experience developing machine learning applications for robotics systems
Strong programming skills in Python, C++, and relevant ML frameworks (TensorFlow, PyTorch)
Experience with reinforcement learning, computer vision, and motion planning algorithms
Familiarity with ROS (Robot Operating System) or similar robotics frameworks
Knowledge of control systems and robot kinematics
Strong mathematical background in linear algebra, calculus, and statistics
Experience with simulation tools for robotics (Gazebo, MuJoCo, Isaac Sim, etc.)
Proven track record of implementing ML solutions in robotics applications
Experience with legged robot locomotion and dynamic stability control

Nice To Haves

Experience with humanoid robots or complex multi-joint robotic systems
Knowledge of model predictive control (MPC) for locomotion
Familiarity with trajectory optimization for legged robots
Knowledge of industrial environments (construction, energy, mining, or manufacturing)
Understanding of safety considerations for learning-based robotic systems
Experience with edge computing and deploying ML models on embedded systems
Familiarity with human-robot interaction paradigms
Background in imitation learning or learning from demonstration
Experience with SLAM (Simultaneous Localization and Mapping) techniques
Publications in relevant conferences (ICRA, IROS, NeurIPS, etc.)

Responsibilities

Create and optimize learning pipelines for training locomotion policies that generalize across environments
Develop and implement machine learning models for robot perception, decision-making, and motion planning
Create and optimize computer vision systems for environmental awareness and object recognition
Collaborate with the hardware team to integrate learning systems with the physical platforms
Design simulation environments for training and testing learning algorithms
Collect, process, and analyze field data to improve learning systems continuously
Develop metrics and benchmarks to evaluate robot learning performance
Stay current with the latest research and innovations in robotics and machine learning