Lead Research Engineer, Anduril Industries

• PhD or Master’s degree in Computer Science, Robotics, Machine Learning, or a related field, or equivalent practical experience
• Proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow, JAX)
• Experience as a Principal Investigator in a research-driven environment
• Solid experience with reinforcement learning methods and their application to autonomous systems
• Strong programming skills in Python and familiarity with C/C++ is a plus
• Demonstrated track record in developing and deploying ML models for complex systems
• Experience with simulation or real-world validation for autonomous vehicles is highly desirable

Desirable

• The Air Dominance & Strike team at Anduril develops aerial and multi-domain robotic systems
• The team is responsible for taking products like Fury (unmanned fighter jet) and Barracuda (air-breathing cruise missile) from concept to product
• The team also develops Lattice for Mission Autonomy, Anduril’s premier software platform that enables masses of Fury, Barracuda, and other first and third party robots to collaborate across various missions
• We work in close coordination with specialist teams like Perception, Motion Planning, Hardware, and Test Engineering to solve some of the hardest problems facing our customers
• We are looking for software engineers and roboticists excited about creating a powerful autonomy software stack that includes computer vision, motion planning, SLAM, controls, estimation, and secure communications
• We are seeking a Research Engineer to drive innovations in autonomous vehicle technology using deep learning and reinforcement learning
• In this dynamic role, you will design state-of-the-art algorithms and systems that enable safe, efficient, and intelligent autonomous driving
• You’ll work at the intersection of simulation, perception, and decision-making to push the boundaries of what’s possible in autonomous mobility
• Design and implement deep learning and reinforcement learning algorithms to improve sensor perception, prediction, and decision-making for autonomous vehicles
• Collaborate with cross-functional teams to integrate research prototypes into robust, production-ready systems including simulation environments and real-world platforms
• Conduct research into reinforcement learning strategies and deep architectures, iterate on experimental designs, and evaluate performance using rigorous quantitative metrics
• Utilize real-world and synthetic data to enhance model robustness and generalization, leveraging scalable training pipelines on distributed systems