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AI Chief Engineering Lead

Andurilindustries
1 day ago
Full-time
Remote
Worldwide
Remote Engineering

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.

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.

What You’ll Do

  • 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.

Required Qualifications

  • Sophisticated knowledge of LLM's with an understanding of how they work and how they're applied
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