Senior Machine Learning Engineer, RL / Locomotion
About The Position
Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. The Anduril Frontier Systems team is building the next generation of robotic platforms for defense and industrial applications. We are a small, high-performing team of roboticists, ML engineers, and systems engineers delivering real-world capability to operators in the field. Our systems operate in unstructured, contested environments where robustness and reliability are non-negotiable. We seek to deliver validated, integrated capabilities to the Department of Defense and the Intelligence Community. We are seeking a Senior Machine Learning Engineer who specializes in RL / Locomotion to develop and deploy locomotion policies for legged robotic platforms. You will own the full pipeline from simulation training to real-world deployment, building systems that enable robust mobility across challenging terrain - rubble, stairs, slopes, and degraded environments. Your work will directly determine whether our platforms can operate where warfighters need them.
Requirements
- 3 - 8+ years of experience with reinforcement learning for legged or mobile robots
- Strong background in dynamics, control, and robot locomotion
- Experience with RL algorithms (PPO, SAC) and training frameworks (rsl_rl, Stable Baselines, rl_games)
- Hands-on experience with physics simulation (Isaac Gym, MuJoCo, PyBullet)
- Demonstrated sim-to-real transfer on physical robotic systems
- Proficiency in Python and PyTorch
- Eligible to obtain and maintain a U.S. security clearance
Nice To Haves
- Experience with bipedal or multi-limbed robotic platforms
- Experience with NVIDIA Isaac Lab or Omniverse
- Publications in top robotics venues (RSS, CoRL, ICRA, IROS)
- Prior work in defense technology or startups
Responsibilities
- Design, train, and deploy reinforcement learning policies for legged robot locomotion using GPU-parallelized simulation (Isaac Gym / Isaac Lab)
- Develop terrain curriculum and domain randomization strategies that produce policies robust to real-world conditions
- Own the sim-to-real transfer pipeline, identifying and closing reality gaps
- Train policies for stair climbing, rough terrain traversal, payload carry, push recovery, and fall recovery
- Define and evaluate performance metrics for locomotion robustness
- Collaborate with manipulation and perception engineers to integrate locomotion into a full autonomy stack
Benefits
- Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package.
- top-tier benefits for full-time employees
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
