AI Chief Engineering Lead, Anduril Industries

• Sophisticated knowledge of LLM's with an understanding of how they work and how they're applied
• Solid experience with reinforcement learning methods and their application to autonomous systems
• Proven experience of shipping products end to end
• Experience with simulation or real-world validation for autonomous vehicles is highly desirable
• A degree in Computer Science, Robotics, Machine Learning, or a related field, or equivalent practical experience
• Eligible to obtain and maintain an active U.S. Top Secret security clearance
• Travel up to 40+% of time to build, test, and deploy capabilities in the real world

Desirable

• We are seeking a Generative AI Chief Engineering Lead 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 capabilities
• Today, employing mass quantities of autonomous robots requires heavy human oversight and execution. Anduril is leveraging AI approaches to improve effectiveness of autonomous missions, offload operator burden, and speed up execution via realtime monitoring, recommendations to users, and multi-modal interaction patterns
• You will apply proven and un-proven approaches to create prototypes for expanding the capability of autonomous systems
• Develop Advanced Agentic Software - Design and implement novel agent-based software systems to improve sensor perception, prediction, and decision-making for autonomous vehicles
• Apply Agentic Reasoning - Design and implement integrated agents and AI models to solve for end-user autonomous systems workflows
• End-to-End System Integration - Collaborate with cross-functional teams to integrate research prototypes into robust, production-ready systems including simulation environments and real-world platforms
• Research & Experimentation - Conduct research into reinforcement learning strategies and deep architectures, iterate on experimental designs, and evaluate performance using rigorous quantitative metrics
• Data-Driven Innovation - Utilize real-world and synthetic data to enhance model robustness and generalization, leveraging scalable training pipelines on distributed systems