Senior Machine Learning Engineer

Anduril Industries is seeking a Senior Machine Learning Engineer to help production operations adopt ML systems that are hardened for real manufacturing conditions. This role focuses on taking ML prototypes to reliable, production-grade deployments across cloud and on-premises OT environments.

Role Focus

You will own production hardening and deployment for ML prototype applications used in manufacturing operations. In this position, you serve as the on-call owner for deployed ML systems and build end-to-end ML platform capabilities spanning datasets, training orchestration, inference, and lifecycle management across factory floor infrastructure.

Key Responsibilities

• Own production hardening and deployment of ML prototype applications, converting research ideas into reliable, maintainable production systems
• Participate in on-call rotations for deployed ML products running on factory floors, supporting Mantech with high availability and rapid incident response
• 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 operational SLOs for production ML applications
• Define and execute rollback procedures, validation gates, and safety reviews for AI systems in production
• Interface with the data engineering team to define and deliver ML-specific data infrastructure needs for Applied Intelligence
• Coordinate with Anduril enterprise teams to secure compute resources, labeling infrastructure, data access, and modeling capabilities for ML initiatives
• Translate ML engineers’ technical requirements into concrete infrastructure for datasets, training pipelines, and inference systems
• Partner with manufacturing engineers and factory operators to translate 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 for production use, supporting real-time and batch execution across cloud, edge, and shop-floor systems
• Implement event-driven data pipelines and telemetry systems to provide contextualized, real-time signals from factory sensors and production systems
• Select and standardize MLOps components such as feature stores, vector databases, model registries, orchestration layers, and observability systems
• Design training infrastructure that enables fast iteration while preserving reproducibility and governance
• Own dataset creation, curation, versioning, and management processes
• Implement model governance for validation, traceability, retraining triggers, and version control
• Build systems for data labeling workflows, quality assurance, and continuous data pipeline monitoring
• Manage model versioning, experiment tracking, and artifact management across development and production environments
• Write production-quality code with comprehensive tests, participating in code review and architectural discussions
• Establish best practices for MLOps, observability, data management, and secure handling of sensitive production data
• Leverage AI tooling (coding assistants, automation) and contribute to engineering practices
• Mentor junior engineers and data scientists on production ML best practices

Required Qualifications

• 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 working 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 (high availability, low latency, constrained environments)
• Proven 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 is required due to the need to access 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 powering Anduril’s shipbuilding factories, including data infrastructure, manufacturing execution system (ArsenalOS), scheduling engine, and AI systems.

MDP operates at the boundary between Operational Technology and Information Technology, where factory-floor machines, edge compute, and OT networks connect with enterprise platforms and cloud infrastructure. MDP incubates solutions close to the production line, validates them with real operators building real hardware, hardens them for reliability and security, and then scales them across multiple sites. The team is scaling from a founding group to 70+ engineers across multiple U.S. sites.

Job Details

• Location: Santa Ana, CA (onsite)
• Compensation: USD 220,000 - 292,000 per yearly
• Minimum Experience: 6 years
• Education: Degree in Computer Science, Engineering, or related technical field (or equivalent practical experience)

About the Applied Intelligence Initiative

Within Applied Intelligence, you will bridge ML research and production operations by owning the infrastructure that moves ML prototypes from concept to reliable, production-grade systems on factory floors. The work connects data engineering, MLOps, and production operations, with coordination across data engineering, Big Anduril, and manufacturing operations. Deployed AI applications must operate 24/7 in real manufacturing environments where downtime has material impact.

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 environments
• Experience with workflow/orchestration 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 (for example, central ML platform teams)
• Familiarity with event-driven architectures, IoT/UNS patterns, and real-time systems integration
• Knowledge of edge ML deployment, model optimization (quantization, pruning), or deploying models on 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

Anduril offers a comprehensive, competitive benefits package (available at little to no cost to employees). Additional details are referenced via Explore Our Benefits.

Recruitment Scam Protection

• No financial requests: Anduril will never solicit payment or request personal financial details at any stage of the hiring process
• Verify communications: recruiter emails will only come from an @anduril.com address
• Verify agency authenticity: reach out to [email protected]
• Confirm sender domain: exercise caution with unsolicited outreach and confirm the sender email domain is @anduril.com
• Report suspicious activity: report suspicious or fraudulent outreach immediately to [email protected]

Data Privacy

To view Anduril’s candidate data privacy policy, please visit https://anduril.com/applicant-privacy-notice/. By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks.