Machine Learning/MLOps Engineer

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.

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.

• 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.

• 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 TensorFlow) 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.
• Experience working with off-the-shelf models (OCR/IDP, CV, STT, RAG) and integrating them into workflow pipelines.
• Familiarity with event-driven architectures, IoT or UNS patterns, and integration with enterprise systems.
• Experience with APIs, schema-based integration, and data contracts.
• Strong problem-solving skills with an ability to simplify workflows into modular, reusable automation components.
• Eligible to obtain and maintain an active U.S. Secret security clearance.

• Experience automating workflows in manufacturing, logistics, or enterprise business processes.
• Experience with workflow orchestration tools (Airflow, Flyte, Prefect, Temporal).
• Familiarity with data engineering concepts, time-series data, and semantic/ontology-driven data structures.
• Exposure to observability systems (Prometheus, Grafana, ELK) for monitoring workflow reliability or model performance.
• Experience integrating AI models with PLM, MES, ERP, CMMS, or similar industrial systems.
• Interest in developing adaptive, human-in-the-loop workflow automations that blend ML, rules, and operational context.