Anduril Industries is seeking to operationalize advanced machine learning models and architect an industrial AI stack to bring intelligence into production systems within their digital shipbuilding environment. This involves selecting, integrating, and standardizing technologies to solve real factory scenarios like inspection, root-cause analysis, and scheduling, thereby improving efficiency and capability in the maritime domain.
Requirements
- 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.
Responsibilities
- 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.
Other
- Strong stakeholder management skills with proven experience aligning engineering, data, and manufacturing teams.
- 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.
- Experience applying AI/ML within manufacturing, logistics, industrial control, or production environments.
- Demonstrated ability to mentor engineers and set technical direction for AI/ML infrastructure at scale.
