Staff Machine Learning/MLOps Engineer

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 Staff 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.

What you’ll do

• 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.

Required qualifications

• 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.

PREFERRED QUALIFICATIONS

• Experience applying AI/ML within manufacturing, logistics, industrial control, or production environments.
• Background with digital twins, predictive maintenance, OCR/IDP, CV, or STT model integrations.
• Experience with workflow/orchestration tools such as Flyte, Airflow, Kubeflow, or Temporal.
• Familiarity with GPU acceleration (CUDA) and inference optimization (TensorRT, Triton).
• Experience in regulated environments (NNPI/ITAR) and secure model/data governance.
• Demonstrated ability to mentor engineers and set technical direction for AI/ML infrastructure at scale.