Senior Machine Learning/MLOps Engineer

Requirements

• Strong stakeholder management skills with proven experience aligning engineering, data, and manufacturing teams,
• 8+ years of experience in software or ML engineering with end-to-end delivery of production-grade AI/ML systems,
• Deep experience with MLOps: data acquisition, labeling, curation, pipeline management, model versioning, continuous integration, and model monitoring,
• Strong proficiency in Python and experience with deep learning frameworks (PyTorch, TensorFlow),
• Experience building and deploying containerized ML services using Docker and Kubernetes,
• Proficiency in data engineering, time-series data modeling, and working with semantic/ontology-driven data systems,
• Experience implementing observability for model performance, inference accuracy, and data drift,
• Familiarity with event-driven architectures, IoT/UNS patterns, and real-time systems integration,
• Excellent communication and documentation abilities; able to bridge research, platform, and production domains,
• Eligible to obtain and maintain an active U.S. Secret security clearance,
• (Desirable) Experience applying AI/ML within manufacturing, logistics, industrial control, or production environments,
• (Desirable) Background with digital twins, predictive maintenance, OCR/IDP, CV, or STT model integrations,
• (Desirable) Experience with workflow/orchestration tools such as Flyte, Airflow, Kubeflow, or Temporal,
• (Desirable) Familiarity with GPU acceleration (CUDA) and inference optimization (TensorRT, Triton),
• (Desirable) Experience in regulated environments (NNPI/ITAR) and secure model/data governance,
• (Desirable) Demonstrated ability to mentor engineers and set technical direction for AI/ML infrastructure at scale

What the job involves

• 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 Senior Machine Learning/MLOps Engineer to join the Applied Intelligence team within Maritime Digital Production,
• You will lead the design, deployment, and sustainment of the AI-enabled backbone that connects data, tools, and people across our digital shipbuilding environment,
• This role focuses on operationalizing advanced models and architecting the industrial AI stack—selecting, integrating, and standardizing technologies that bring intelligence into production systems only where they deliver real value,
• You’ll define and implement core components of the platform, including unified data and feature stores, vector databases, orchestration layers, and model-serving frameworks, while integrating off-the-shelf models for computer vision, OCR/IDP, speech, and retrieval-augmented generation,
• You’ll work across software, data, manufacturing, and corporate technology teams to translate real factory scenarios—inspection, root-cause analysis, receiving workflows, scheduling—into applied AI capabilities with clear human-in-the-loop controls, auditability, safety gates, and integrations with PLM, MES, CMMS, ERP, and the unified data plane,
• This role demands technical range, judgment, and an instinct for when AI is the right tool versus when a simpler logic or workflow solution wins,
• You’ll collaborate across Anduril in a high-tech, fast-paced culture of innovation focused on delivering systems that work in the real world,
• If you want to shape how AI becomes a practical, reliable tool for production and logistics at scale, you’ll be helping build the future of digital shipbuilding and the next generation of maritime vehicles,
• Architect and own the AI/ML platform stack—from data ingestion, labeling, and feature engineering to model training, deployment, monitoring, and lifecycle management,
• Select, prioritize, and standardize industrial AI components including feature stores, vector databases + RAG, OCR/IDP and CV/STT providers, orchestration layers, and observability systems,
• Build model-serving and inference frameworks optimized for production environments, supporting real-time and batch execution across cloud, edge, and shop-floor systems,
• Translate factory scenarios (receiving, inspection, RCCA, scheduling) into applied AI workflows with defined human-in-the-loop gates, audit trails, and integration contracts with PLM, MES, CMMS, ERP, and the unified data plane,
• Implement event-driven data pipelines and telemetry systems that feed models with contextualized, real-time signals from production and logistics systems,
• Drive make/buy strategy by researching internal and vendor AI capabilities and recommending investments aligned to enterprise roadmaps, Anduril IP principles, and production constraints,
• Define and maintain model governance processes for validation, safety reviews, traceability, and rollback,
• Partner with Maritime platform teams and CorpTech to align architectures with enterprise data standards, ontologies, and compliance policies,
• Lead reliability engineering for deployed models—managing drift detection, retraining triggers, alerting, and operational SLOs,
• Mentor junior engineers and data scientists; establish best practices for MLOps, observability, data management, and secure handling of sensitive production data