Senior Machine Learning Engineer (Applied Intelligence)
$220k - $292k • Santa Ana, California, United States
Posted 2d ago
Job Location
Santa Ana, California, United States
Tech Stack
Remote Work Policy
On-site
Categories
Machine Learning Engineer
About the job
Anduril Industries is a defense technology company focused on transforming military capabilities with advanced technology. The Maritime Digital Production (MDP) team builds and deploys the full technology stack for Anduril's shipbuilding factories, including data infrastructure, manufacturing execution systems, scheduling engines, and AI systems. This role is for a Machine Learning Engineer on the Applied Intelligence initiative, acting as a bridge between ML research and production operations. You will own the infrastructure that brings ML prototypes to production-grade systems, working at the intersection of data engineering, MLOps, and production operations. The role requires translating ML requirements into production infrastructure, coordinating across teams, and serving as an on-call owner for deployed ML systems, ensuring AI applications function reliably 24/7 in real manufacturing environments.
Responsibilities
- Own production hardening and deployment of ML prototype applications, transforming research concepts into reliable, maintainable production systems.
- Participate in on-call rotations for deployed ML products running on factory floors, 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.
- Interface directly with the data engineering team to define and deliver ML-specific data infrastructure needs.
- Coordinate with enterprise teams to secure compute resources, labeling infrastructure, data access, and modeling capabilities.
- Translate ML engineers' 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.
- 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.
Requirements
- Ability to translate ML requirements into production infrastructure.
- Ability to coordinate across teams (data engineering, enterprise, manufacturing operations).
- Ability to serve as the on-call owner for deployed ML systems.
- Experience ensuring AI applications work 24/7 in real manufacturing environments where downtime has real consequences.