Generative AI Chief Engineering Lead

Anduril Anduril · Defense · Washington, DC · Air Dominance & Strike : Mission Autonomy Engineering : Mission Software Engineering

Lead engineering for generative AI and reinforcement learning in autonomous vehicle technology, focusing on agentic software systems, end-to-end integration, and data-driven innovation using real-world and synthetic data.

What you'd actually do

  1. Develop Advanced Agentic Software - Design and implement novel agent-based software systems to improve sensor perception, prediction, and decision-making for autonomous vehicles
  2. Apply Agentic Reasoning - Design and implement integrated agents and AI models to solve for end-user autonomous systems workflows.
  3. 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.
  4. Research & Experimentation - Conduct research into reinforcement learning strategies and deep architectures, iterate on experimental designs, and evaluate performance using rigorous quantitative metrics.
  5. Data-Driven Innovation** **-** **Utilize real-world and synthetic data to enhance model robustness and generalization, leveraging scalable training pipelines on distributed systems.

Skills

Required

  • 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
  • Eligible to obtain and maintain an active U.S. Top Secret security clearance

Nice to have

  • Experience with simulation or real-world validation for autonomous vehicles
  • PhD or Master’s degree in Computer Science, Robotics, Machine Learning, or a related field, or equivalent practical experience
  • Novel application track record and experience including first author publications, participation in peer reviewed conferences, contribution to open source projects, and demonstrated contribution to the ML and AI community.
  • Proven experience in deep learning research and development, particularly in generative AI. This includes diffusion models and autoregressive generative models.
  • Experience in multi-modal sensor data processing (e.g., cameras, LiDAR, radar).
  • Familiarity with ML Ops best practices, including model versioning and reproducible research pipelines.
  • Strong programming skills in Python and familiarity with C/C++ is a plus.
  • General software engineering experience solving motion planning or related robotics problems.

What the JD emphasized

  • Proven experience of shipping products end to end
  • Eligible to obtain and maintain an active U.S. Top Secret security clearance

Other signals

  • Generative AI
  • autonomous vehicles
  • reinforcement learning
  • agent-based software systems
  • LLMs