Senior Machine Learning Engineer; Applied Intelligence

Maritime Digital Production (MDP) is the software and digital systems function within Anduril's Heavy Metal division. We build and deploy the full technology stack that powers Anduril's shipbuilding factories: the data infrastructure that makes every machine and sensor visible in real time, the manufacturing execution system (ArsenalOS) that workers and planners use every shift, the scheduling engine that replans production in minutes instead of days, and the AI systems that eliminate manual toil from both the shop floor and business operations.

MDP operates at the boundary between Operational Technology and Information Technology. Our systems live where factory-floor machines, edge compute, and OT networks meet enterprise platforms and cloud infrastructure. We incubate solutions close to the production line, validate them with real operators building real hardware, harden them for reliability and security, and then scale them across multiple sites. The environment is fast, physical, and consequential.

When our systems go down, production stops. The output of our work is not a dashboard: it is a ship.

This is not a support function. It is a strategic investment by Anduril in the premise that digitizing the manufacturing lifecycle end-to-end, from engineering definition through scheduling through execution through field feedback, is how Heavy Metal will out-build, out-adapt, and out-scale the traditional defense industrial base. MDP is scaling from a founding team to 70+ engineers across multiple U.S. sites. You will be joining early, working on hard problems with real operational stakes, and shaping how manufacturing software is built at Anduril from the ground up.

As a Machine Learning Engineer on the Applied Intelligence initiative, you will be the critical bridge between ML research and production operations. You will own the infrastructure that brings ML prototypes from concept to reliable, production-grade systems running on factory floors and serving manufacturing teams.

This role requires someone who can translate ML requirements into production infrastructure, coordinate across teams (data engineering, Big Anduril, manufacturing operations), and serve as the on-call owner for deployed ML systems. You'll be responsible for ensuring that AI applications don't just work in demos—they work 24/7 in real manufacturing environments where downtime has real consequences.

Infrastructure Ownership & Production Operations

• Own production hardening and deployment of ML prototype applications, transforming research concepts into reliable, maintainable production systems
• Take part in on-call rotations for deployed ML products running on factory floors and serving Mantech, ensuring high availability and rapid incident response
• Build and maintain inference infrastructure across cloud environments and on-premises OT hardware deployed on the factory floor
• Deploy and operate ML systems in factory environments, including edge compute clusters and OT networks, with a focus on reliability and observability
• Implement comprehensive monitoring, alerting, and drift detection systems to maintain operational SLOs for production ML applications
• Define and execute rollback procedures, validation gates, and safety reviews for AI systems in production

Cross-Functional Coordination & Requirements Translation

• Interface directly with the data engineering team to define and deliver ML-specific data infrastructure needs for Applied Intelligence
• Coordinate with Anduril enterprise teams to secure compute resources, labeling infrastructure, data access, and modeling capabilities needed for ML initiatives
• Execute the technical vision of ML engineers by translating their requirements into…