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.

As a Machine Learning Engineer on the Applied Intelligence initiative, you will be the critical bridge between ML research and production operations.

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 engineering practices
• Mentor junior engineers and data scientists on production ML best practices