Chief Machine Learning Engineer Lead

We are seeking a Chief Machine Learning Engineer 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 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 Algorithms - Design and implement deep learning and reinforcement learning algorithms 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

• PhD or Master’s degree in Computer Science, Robotics, Machine Learning, or a related field, or equivalent practical experience.
• Demonstrated proficiency in deep learning frameworks (e.g., PyTorch, TensorFlow, JAX) including LLM fine-tuning.
• Solid experience with reinforcement learning methods and their application to autonomous systems.
• Experience with simulation or real-world validation for autonomous vehicles is highly desirable.
• Proven experience in deep learning research and development, particularly in generative AI. This includes diffusion models and autoregressive generative models.
• Eligible to obtain and maintain an active U.S. Top Secret security clearance
• Travel up to 30% of time to build, test, and deploy capabilities in the real world

Preferred Qualifications

• 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.
• 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.