Senior Machine Learning Engineer

Anduril Industries is hiring an ML engineer to harden and deploy production machine learning systems for manufacturing operations at the factory floor level.

Responsibilities

• Own production hardening and deployment of ML prototype applications, converting research concepts into dependable and maintainable production systems
• Participate in on-call rotations for deployed ML products running on factory floors, supporting Mantech and responding to production incidents to maintain high availability
• Build and maintain inference infrastructure across cloud environments and on-premises OT hardware deployed on the factory floor
• Deploy and operate ML systems in factory environments, including edge compute clusters and OT networks, with a focus on reliability and observability
• Implement monitoring, alerting, and drift detection to maintain production ML operational SLOs
• Define and execute rollback procedures, validation gates, and safety reviews for AI systems in production
• Work directly with the data engineering team to define and deliver ML-specific data infrastructure needs for Applied Intelligence
• Coordinate with enterprise teams to secure compute resources, labeling infrastructure, data access, and modeling capabilities for ML initiatives
• Translate ML engineers’ technical requirements into infrastructure for datasets, training pipelines, and inference systems
• Partner with manufacturing engineers and factory operators to turn production workflows into MLOps requirements
• Drive make/buy decisions by evaluating internal and vendor capabilities against Applied Intelligence needs
• Architect and own the end-to-end ML platform stack, including data ingestion and labeling, training orchestration, model serving, and lifecycle management
• Build model-serving and inference frameworks optimized for production, supporting both real-time and batch execution across cloud, edge, and shop-floor systems
• Implement event-driven data pipelines and telemetry systems that provide models with contextualized real-time signals from factory sensors and production systems
• Select, prioritize, and standardize MLOps components such as feature stores, vector databases, model registries, orchestration layers, and observability systems
• Design training infrastructure to enable rapid iteration with reproducibility and governance
• Own dataset creation, curation, versioning, and management processes for high-quality model development
• Implement model governance for validation, traceability, retraining triggers, and version control
• Build systems for data labeling workflows, quality assurance, and continuous monitoring of data pipelines
• Manage model versioning, experiment tracking, and artifact management across development and production environments
• Write production-quality code with comprehensive tests, participating in code reviews and architectural discussions
• Establish best practices for MLOps, observability, data management, and secure handling of sensitive production data
• Use AI tooling (coding assistants, automation) and contribute to team engineering practices
• Mentor junior engineers and data scientists on production ML best practices

Requirements

• 6+ years of experience in software engineering or ML engineering roles building production systems, ideally in fast-paced environments
• Strong production MLOps experience, including data pipeline management, model versioning, deployment automation, and production monitoring
• Proven track record taking ML models from prototype to production and maintaining them long-term
• Experience with on-call rotations and production incident management for data or ML systems
• Strong Python proficiency and experience with deep learning frameworks (PyTorch, TensorFlow)
• Experience building and deploying containerized ML services using Docker and Kubernetes
• Solid data engineering skills, including pipeline orchestration, data quality management, and work with time-series or event-driven data
• Experience implementing observability for model performance, inference accuracy, and data drift
• Strong technical communication skills across ML research, data engineering, and operations teams
• Experience building systems that operate reliably under real-world constraints such as high availability, low latency, and constrained environments
• Ability to coordinate across multiple teams and translate requirements into technical solutions
• Degree in Computer Science, Engineering, or related technical field (or equivalent practical experience)
• U.S. Person status required due to access to export controlled data

Technologies

• Python, PyTorch, TensorFlow
• Docker, Kubernetes
• Flyte, Airflow, Kubeflow, Temporal
• TensorRT, Triton Inference Server
• MLflow, Weights & Biases
• Feast, Tecton, Pinecone, Weaviate, Milvus
• RAG, GPU infrastructure management
• Quantization, pruning

About the Team

• Maritime Digital Production (MDP) is the software and digital systems function within Anduril’s Heavy Metal division
• MDP builds and deploys the technology stack for Anduril’s shipbuilding factories, including data infrastructure, manufacturing execution system (ArsenalOS), scheduling engine, and AI systems
• MDP operates at the boundary between Operational Technology (OT) and Information Technology (IT), where factory-floor machines, edge compute, and OT networks meet enterprise platforms and cloud infrastructure
• MDP incubates solutions close to the production line, validates with real operators building real hardware, hardens for reliability and security, then scales across multiple sites
• When systems go down, production stops
• MDP is scaling from a founding team to 70+ engineers across multiple U.S. sites

About the Job

• Applied Intelligence role bridges ML research and production operations
• Own infrastructure that moves ML prototypes from concept to production-grade systems running on factory floors and serving manufacturing teams
• Work at the intersection of data engineering, MLOps, and production operations
• Build robust datasets, training infrastructure, and inference systems for reliable operation in both cloud and on-premises OT environments
• Coordinate across data engineering, Big Anduril, and manufacturing operations teams; serve as on-call owner for deployed ML systems
• AI applications must work 24/7 in real manufacturing environments where downtime has real consequences

Preferred Qualifications

• Experience in manufacturing, industrial, or OT-adjacent domains (MES, SCADA, factory automation, IoT)
• Background deploying ML models to edge devices or on-premises hardware in production
• Experience with orchestration/workflow tools such as Flyte, Airflow, Kubeflow, or Temporal
• Familiarity with GPU infrastructure management and inference optimization (TensorRT, Triton Inference Server)
• Experience with MLOps tools including experiment tracking (MLflow, Weights & Biases), feature stores (Feast, Tecton), and model registries
• Experience building RAG systems, vector databases (Pinecone, Weaviate, Milvus), or LLM deployment pipelines
• Background with computer vision model deployment, OCR/IDP systems, or document processing pipelines
• Experience coordinating with platform or infrastructure teams at larger organizations
• Familiarity with event-driven architectures, IoT/UNS patterns, and real-time integration
• Knowledge of edge ML deployment and model optimization (quantization, pruning), including resource-constrained devices
• Experience in regulated environments (NNPI/ITAR) and secure model/data governance
• Experience in hyper-growth startup environments, balancing speed, ambiguity, and long-term system health
• Familiarity with enterprise systems such as ERP, MES, WMS, or PLM
• Eligible to obtain and maintain a U.S. Secret security clearance

Benefits

• Comprehensive, competitive benefits package (details referenced via “Explore Our Benefits”)

Recruitment Scam Protection

• No financial requests: Anduril will never solicit payment or demand personal financial details during hiring
• Verify recruiter emails come from an @anduril.com address
• Verify agency authenticity by reaching out to [email protected]
• Exercise caution with unsolicited outreach and confirm the sender email domain is @anduril.com
• Report suspicious or fraudulent outreach immediately to [email protected]

Data Privacy

• Candidate data privacy policy: https://anduril.com/applicant-privacy-notice/
• Submitting an application consents to third-party service provider screening for pre-employment risk, integrity, and due diligence

Location: Santa Ana, CA (onsite)

Salary: USD 220,000 - 292,000 per yearly