Senior Machine Learning Engineer, Applied Intelligence

ABOUT THE TEAM

Maritime Digital Production (MDP) is the software and digital systems function within Anduril's Heavy Metal division. We build and deploy the full technology stack that powers Anduril's shipbuilding factories: the data infrastructure that makes every machine and sensor visible in real time, the manufacturing execution system (ArsenalOS) that workers and planners use every shift, the scheduling engine that replans production in minutes instead of days, and the AI systems that eliminate manual toil from both the shop floor and business operations.

MDP operates at the boundary between Operational Technology and Information Technology. Our systems live where factory-floor machines, edge compute, and OT networks meet enterprise platforms and cloud infrastructure. We incubate solutions close to the production line, validate them with real operators building real hardware, harden them for reliability and security, and then scale them across multiple sites. The environment is fast, physical, and consequential. When our systems go down, production stops. The output of our work is not a dashboard: it is a ship.

This is not a support function. It is a strategic investment by Anduril in the premise that digitizing the manufacturing lifecycle end-to-end, from engineering definition through scheduling through execution through field feedback, is how Heavy Metal will out-build, out-adapt, and out-scale the traditional defense industrial base. MDP is scaling from a founding team to 70+ engineers across multiple U.S. sites. You will be joining early, working on hard problems with real operational stakes, and shaping how manufacturing software is built at Anduril from the ground up.

ABOUT THE JOB

As a Senior ML Engineer on the Applied Intelligence initiative, you will help architect and operate the AI/ML platform stack that powers ML pipelines for factory sensing, document processing, and intelligent automation. You will help build the infrastructure that operationalizes computer vision, NLP, and RAG-enabled tools, translating factory scenarios into production-grade AI workflows with clear human-in-the-loop controls and enterprise system integrations.

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 for factory sensing and intelligent automation applications.
• Select, prioritize, and standardize industrial AI components including feature stores, vector databases for RAG pipelines, OCR/IDP and computer vision model serving, 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.
• Partner with manufacturing engineers and factory operators to understand production workflows and translate them into MLOps requirements.
• Write production-quality code with comprehensive tests, participating in code review and architectural discussions.
• Translate factory scenarios (quality inspection, receiving, root-cause analysis, document processing) into applied AI workflows with defined human-in-the-loop gates, audit trails, and integration contracts with PLM, MES, ERP, and the unified data plane.
• Implement event-driven data pipelines and telemetry systems that feed models with contextualized, real-time signals from factory sensors, production systems, and logistics operations.
• Deploy and operate your systems in factory environments, including edge compute clusters and OT networks.
• 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 procedures for AI systems in production.
• Lead reliability engineering for deployed models—managing drift detection, retraining triggers, alerting, and operational SLOs for factory sensing and document processing applications.
• Leverage AI tooling (coding assistants, automation) in your development workflow and contribute to team engineering practices.
• Join an on-call rotation supporting production factory systems.
• Mentor junior engineers and data scientists; establish best practices for MLOps, observability, data management, and secure handling of sensitive production data.

REQUIRED QUALIFICATIONS

• 8+ years of experience in a software engineering role building production systems, ideally in a fast-paced environment.
• Deep expertise in MLOps with end-to-end experience delivering production-grade AI/ML systems.
• Strong technical fluency in modern software architectures, APIs, distributed systems, CI/CD, and cloud or edge infrastructure.
• 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.
• Experience building systems that must operate reliably under real-world operational constraints (high availability, low latency, or constrained environments).
• Strong stakeholder management skills with proven experience aligning engineering, data, and manufacturing teams.
• Excellent written and verbal communication skills; able to bridge research, platform, and production domains and collaborate across engineering, manufacturing, and operations teams.
• Degree in Computer Science, Information Systems, Engineering, or related technical field, or equivalent practical experience.
• U.S. Person status is required as this position needs to access export controlled data.

PREFERRED QUALIFICATIONS

• Experience in manufacturing, industrial, or OT-adjacent domains (MES, SCADA, PLC integration, factory automation, IoT).
• Experience applying AI/ML within manufacturing, logistics, industrial control, or production environments.
• Background with digital twins, predictive maintenance, OCR/IDP, computer vision, or speech-to-text model integrations.
• Experience with workflow/orchestration tools such as Flyte, Airflow, Kubeflow, or Temporal.
• Familiarity with GPU acceleration (CUDA) and inference optimization (TensorRT, Triton Inference Server).
• Experience building RAG (Retrieval-Augmented Generation) systems, vector databases (Pinecone, Weaviate, Milvus), and LLM deployment pipelines.
• Familiarity with frontier AI tooling, AI coding assistants, and AI-enabled software development workflows.
• Experience in hyper-growth startup-like environments, with demonstrated success balancing speed, ambiguity, and long-term system health.
• Familiarity with enterprise systems such as ERP, MES, WMS, PLM, or manufacturing planning systems.
• 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.
• Experience with MLOps tools including experiment tracking (MLflow, Weights & Biases), feature stores (Feast, Tecton), and model registries.
• Experience in manufacturing industries with hands-on exposure to assembly lines or production environments.
• Knowledge of edge ML deployment, model optimization (quantization, pruning), or deploying models on resource-constrained devices.
• Eligible to obtain and maintain a U.S. Secret security clearance.