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 using Docker and Kubernetes
• Solid data engineering skills including pipeline orchestration, data quality management, and working with time-series or event-driven data
• Experience implementing observability for model performance, inference accuracy, and data drift
• Strong technical communication skills with ability to work across ML research, data engineering, and operations teams
• Experience building systems that must operate reliably under real-world operational constraints (high availability, low latency, constrained environments)
• Proven ability to coordinate across multiple teams and translate requirements into technical solutions
• Degree in Computer Science, Engineering, or related technical field, or equivalent practical experience
• U.S. Person status is required as this position needs to access export controlled data

PREFERRED QUALIFICATIONS

• Experience in manufacturing, industrial, or OT-adjacent domains (MES, SCADA, factory automation, IoT)
• Background deploying ML models to edge devices or on-premises hardware in production environments
• Experience with workflow/orchestration tools such as Flyte, Airflow, Kubeflow, or Temporal
• Familiarity with GPU infrastructure management and inference optimization (TensorRT, Triton Inference Server)
• Experience with MLOps tools including experiment tracking (MLflow, Weights & Biases), feature stores (Feast, Tecton), and model registries
• Experience building RAG systems, vector databases (Pinecone, Weaviate, Milvus), or LLM deployment pipelines
• Background with computer vision model deployment, OCR/IDP systems, or document processing pipelines
• Experience coordinating with platform or infrastructure teams at larger organizations (e.g., working with central ML platform teams)
• Familiarity with event-driven architectures, IoT/UNS patterns, and real-time systems integration
• Knowledge of edge ML deployment, model optimization (quantization, pruning), or deploying models on resource-constrained devices
• Experience in regulated environments (NNPI/ITAR) and secure model/data governance
• Experience in hyper-growth startup environments, with demonstrated success balancing speed, ambiguity, and long-term system health
• Familiarity with enterprise systems such as ERP, MES, WMS, or PLM
• Eligible to obtain and maintain a U.S. Secret security clearance