Job description

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 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 US Salary Range $220,000 — $292,000 USD The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including: Benefits At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits . Protecting Yourself from Recruitment Scams Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information. To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind: No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates. Please always verify communications: Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com . Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links. What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com . Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts. Data Privacy To view Anduril's candidate data privacy policy, please visit anduril.com . By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks as part of your application process. This third-party service provider provides risk-intelligence services that may include analysis of sanctions and watchlists, adverse media, public-record information, and other lawful open-source or commercial data sources. This third-party service provider does not act as a consumer reporting agency. Use of this provider helps to ensure compliance with applicable laws and protect technology, intellectual property, and organizational security.

Requirements

Must-haves

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

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigomachine learningmlpythondockerkubernetesllm

Platform: greenhouse
Effort: quick

Benefits

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Vision