Senior Machine Learning/mlops Engineer

Anduril Anduril · Defense · Costa Mesa, CA · Maritime & Maneuver Dominance : Heavy Metal - Engineering & Operations : Software Engineering

Senior ML/MLOps Engineer to lead the design, deployment, and sustainment of an AI-enabled backbone for a digital shipbuilding environment. Focuses on operationalizing advanced models and architecting the industrial AI stack, integrating components like feature stores, vector databases, orchestration, and model-serving frameworks. Will translate factory scenarios into applied AI capabilities with human-in-the-loop controls and integrations with production systems.

What you'd actually do

  1. Architect and own the AI/ML platform stack—from data ingestion, labeling, and feature engineering to model training, deployment, monitoring, and lifecycle management.
  2. Select, prioritize, and standardize industrial AI components including feature stores, vector databases + RAG, OCR/IDP and CV/STT providers, orchestration layers, and observability systems.
  3. Build model-serving and inference frameworks optimized for production environments, supporting real-time and batch execution across cloud, edge, and shop-floor systems.
  4. 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.
  5. Implement event-driven data pipelines and telemetry systems that feed models with contextualized, real-time signals from production and logistics systems.

Skills

Required

  • stakeholder management skills
  • 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
  • Eligible to obtain and maintain an active U.S. Secret security clearance

Nice to have

  • 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 engines

What the JD emphasized

  • end-to-end delivery of production-grade AI/ML systems
  • Deep experience with MLOps
  • Experience building and deploying containerized ML services using Docker and Kubernetes
  • Experience implementing observability for model performance, inference accuracy, and data drift

Other signals

  • operationalizing advanced models
  • architecting the industrial AI stack
  • integrating off-the-shelf models
  • applied AI capabilities with clear human-in-the-loop controls
  • auditability, safety gates