Senior Machine Learning Engineer, Perception
Anduril IndustriesWashington, District of Columbia, United StatesPosted 4 April 2026
Job Description
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.
ABOUT THE TEAM
Anduril's Lattice software platform integrates many sensors into a single cohesive view of the battlefield, providing the context our users need to make better decisions, faster. Perception Engineers on Anduril's Frontier AI team build edge-compatible systems that power these views, transforming complex multi-sensor inputs into accurate, actionable understanding of the world. We integrate ambitious machine learning and computer vision algorithms directly into the Lattice platform to increase the fidelity, accuracy, and usefulness of battlefield awareness. These systems improve the experience of Anduril's existing customers, aid in the scaling of our business to new customers, use-cases, and business lines, and they create high-quality snapshots of the world that autonomous and agentic systems can operate on top of.
WHAT YOU'LL DO
Design, train, and deploy computer vision and perception models for edge- compatible, mission-critical environments
Build multi-sensor perception systems that fuse imagery, video, and other sensor data into coherent views of the battlefield
Improve the fidelity, accuracy, and robustness of Lattice's understanding of objects, activities, and environments across varied operational conditions
Integrate state-of-the-art machine learning algorithms into production systems used by customers in real-world scenarios
Develop mission-relevant evaluation frameworks, datasets, and benchmarks to measure perception performance in the field
Partner closely with software, autonomy, product, and deployment teams to turn research ideas into operational capabilities
Identify new perception and battlefield-understanding problems where advances in computer vision can unlock major product impact
REQUIRED QUALIFICATIONS
BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field
5+ years of experience developing computer vision, perception, or machine learning systems in production or advanced research settings
Strong experience with deep learning, object detection, and object tracking frameworks
Strong programming skills in Python, with the ability to build reliable research and production workflows
Strong desire to invent, implement, test, and deploy novel techniques that vastly improve upon state-of-the-art public methods to solve research problem specific to the defense space
Experience training, evaluating, and iterating on vision models for detection, tracking, segmentation, classification, or sensor fusion tasks
Experience deploying or optimizing ML systems for constrained, real-time, or edge compute environments
Ability to work across the full lifecycle of applied ML, from problem formulation and data strategy through deployment and performance monitoring
Eligible to obtain and maintain an active U.S. Secret security clearance
PREFERRED QUALIFICATIONS
Advanced degree with a focus on computer vision, perception, robotics, or machine learning
Experience with multi-modal or multi-sensor fusion systems
Experience deploying deep learning models to embedded, edge, or air-gapped environments
Familiarity with real-time perception systems for autonomous platforms, defense applications, or other safety-critical systems
... (truncated, view full listing at source)
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