Senior Machine Learning Engineer (Applied Intelligence)

Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. 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. 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. Responsibilities 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 concrete infrastructure for datasets, training pipelines, and inference systems. Partner with manufacturing engineers and factory operators to understand production workflows and translate them into MLOps requirements. Drive make/buy decisions by evaluating internal and vendor capabilities against Applied Intelligence needs. Platform & Infrastructure Development Architect and own the end-to-end ML platform stack,from data ingestion and labeling through training orchestration to model serving and lifecycle management. Build model-serving and inference frameworks optimized for production environments, supporting real-time and batch execution across cloud, edge, and shop-floor systems. Implement event-driven data pipelines and telemetry systems that feed models with contextualized, real-time signals from factory sensors and production systems. Select, prioritize, and standardize MLOps components including feature stores, vector databases, model registries, orchestration layers, and observability systems. Design training infrastructure that enables ML engineers to iterate quickly while maintaining reproducibility and governance. Data & Model Lifecycle Management Own dataset creation, curation, versioning, and management processes that enable high-quality model development. Implement model governance processes for validation, traceability, retraining triggers, and version control. Build systems for data labeling workflows, quality assurance, and continuous data pipeline monitoring. Manage model versioning, experiment tracking, and artifact management across development and production environments. Code Quality & Team Practices Write production-quality code with comprehensive tests, participating in code review and architectural discussions. Establish best practices for MLOps, observability, data management, and secure handling of sensitive production data. Leverage AI tooling (coding assistants, automation) in your development workflow and contribute to team enginee