Machine Learning/MLOps Engineer, Anduril Industries

• 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

Desirable

• 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