Machine Learning​/MLOps Engineer

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

Anduril Maritime delivers platforms, systems, and integrated effects in the maritime domain. Our autonomous vehicles (sub‑surface and surface) are the cornerstone of these capabilities, and we continually strive to push the boundaries of the possible in terms of endurance, autonomy and mission capability. The Maritime team develops and maintains core products and payloads, and adapts and applies those products to serve a wide variety of defense, IC and commercial customers in US and international markets.

About the Job

We are seeking a Machine Learning/MLOps Engineer to join the Applied Intelligence team within Maritime Digital Production. You will help build the applied AI and automation systems that streamline business processes and shipyard workflows across design, production, logistics, and quality. This role focuses on operationalizing models, automating manual workflows, and developing the infrastructure that enables AI‑driven decision support across the Build Chain.

You’ll work across software, data, and operational technology domains to develop pipelines, model‑serving components, orchestration logic, and monitoring tools that keep AI‑enabled workflows reliable, auditable, and safe. You will translate real user pain points into automated digital workflows—applying AI only where it adds value and leaning on simpler automation when it doesn’t. Your work will improve throughput, reduce administrative burden, and accelerate decision velocity across the broader Maritime Digital ecosystem.

What You’ll Do

• Develop and maintain data pipelines, feature engineering workflows, and model‑serving components that support applied AI use cases across the yard.

• Implement automation workflows that streamline business and production processes—applying models, logic, and orchestration to remove manual steps and reduce friction.

• Integrate off‑the‑shelf models (OCR/IDP, CV, RAG, STT) into workflow solutions using standardized APIs, datasets, and orchestration layers.

• Build and maintain MLOps pipelines for data ingestion, labeling, versioning, training, evaluation, deployment, monitoring, and rollback.

• Deploy workflow automation and model‑serving components in event‑driven environments integrated with PLM, MES, CMMS, ERP, and unified data layers.

• Contribute to observability tools for monitoring inference performance, data quality, and workflow reliability.

• Collaborate with digital, manufacturing, and corporate technology teams to map current workflows, identify automation opportunities, and integrate solutions safely.

• Ensure all deployed AI/automation workflows include human‑in‑the‑loop gates, audit trails, and compliance features required for production operations.

• Document integration contracts, workflow logic, data flows, and operational runbooks to support scaling and handoff.

Required Qualifications

• Strong stakeholder and cross‑functional communication skills; able to gather workflow requirements and convert them into technical automation.

• 3–6 years of experience in machine learning engineering, MLOps, or backend workflow automation.

• Proficiency in Python and experience with ML frameworks (PyTorch or Tensor Flow) and data processing libraries.

• Experience building and deploying containerized services (Docker; familiarity with Kubernetes preferred).

• Understanding of MLOps practices: data pipelines, model versioning, evaluation, CI/CD for ML, monitoring, and retraining.

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