Senior Machine Learning Engineer (Applied Intelligence)
Anduril Industries · Santa Ana, California, United States
About this role
**Senior Machine Learning Engineer (Applied Intelligence)** **About Anduril** Anduril Industries is a defense technology company transforming U.S. and allied military capabilities with advanced technology. Anduril’s family of systems is powered by **Lattice OS**, an AI-powered operating system that turns thousands of data streams into a real-time, 3D command and control center. **About the Team (Maritime Digital Production / MDP)** Maritime Digital Production (MDP) builds and deploys the full technology stack powering Anduril’s shipbuilding factories—covering data infrastructure, the manufacturing execution system (**ArsenalOS**), scheduling, and AI systems that reduce manual toil across the shop floor and business operations. MDP operates at the boundary between **Operational Technology (OT)** and **Information Technology (IT)**. When systems go down, production stops—so reliability and security are critical. **About the Job** As a Machine Learning Engineer on the **Applied Intelligence** initiative, you’ll bridge ML research and production operations. You’ll own the infrastructure that takes ML prototypes from concept to **reliable, production-grade systems** running on factory floors and supporting manufacturing teams. You’ll work across **data engineering, MLOps, and production operations**, translating ML requirements into production infrastructure and serving as the **on-call owner** for deployed ML systems. --- ## What You’ll Do ### Infrastructure Ownership & Production Operations - Own production hardening and deployment of ML prototype applications, turning research into reliable, maintainable systems - Participate in on-call rotations for deployed ML products on factory floors, ensuring high availability and rapid incident response - Build and maintain inference infrastructure across **cloud** and **on-premises OT** hardware - Deploy and operate ML systems in factory environments (including edge compute clusters and OT networks) with a focus on reliability and observability - Implement monitoring, alerting, and **drift detection** to maintain production ML SLOs - Define rollback procedures, validation gates, and safety reviews for AI systems in production ### Cross-Functional Coordination & Requirements Translation - Partner with data engineering to define and deliver ML-specific data infrastructure needs - Coordinate with enterprise teams for compute resources, labeling infrastructure, data access, and modeling capabilities - Translate ML engineers’ requirements into concrete infrastructure for datasets, training pipelines, and inference systems - Work with manufacturing engineers and factory operators to understand workflows and translate them into MLOps requirements - Drive make/buy decisions by evaluating internal and vendor capabilities ### Platform & Infrastructure Development - Architect and own the end-to-end ML platform stack (data ingestion/labeling → training orchestration → model serving/lifecycle management) - Build model-serving and inference frameworks for real-time and batch execution across cloud, edge, and shop-floor systems - Implement event-driven data pipelines and telemetry systems feeding models with contextualized, real-time signals from sensors and production systems - Select and standardize MLOps components (e.g., feature stores, vector databases, model registries, orchestration, observability) - Design training infrastructure that enables fast iteration while maintaining reproducibility and
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