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. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.

ABOUT THE TEAM

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.

ABOUT THE JOB

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. Working at the intersection of data engineering, MLOps, and production operations, you'll execute the vision of ML engineers by building robust datasets, training infrastructure, and inference systems that operate reliably in both cloud and on-premises OT environments. 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.

WHAT YOU'LL DO

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

REQUIRED QUALIFICATIONS

6+ years of experience in software engineering or ML engineering roles building production systems, ideally in fast-paced environments

Strong production MLOps experience: data pipeline management, model versioning, deployment automation, and production monitoring

Proven track record of taking ML models from prototype to production and maintaining them long-term

Experience with on-call rotations and production incident management for data or ML systems

Strong proficiency in Python and experience with deep learning frameworks (PyTorch, TensorFlow)

Experience building and deploying containerized ML services