Senior Machine Learning Engineer

Anduril Industries; Santa Ana (CA); On-site; USD 220,000 - 292,000; Full time

Job summary

Anduril Industries in Santa Ana, CA, seeks an experienced ML/Software engineer to own the production hardening and deployment of ML prototype applications for manufacturing operations. You will bridge ML research with production-grade systems across cloud and on-prem OT environments, and serve as the on-call owner for deployed ML systems.

You will build end-to-end ML platform capabilities and collaborate with data engineers, manufacturing engineers, and enterprise teams to ensure reliable,

Qualifications

• 6+ years of experience building production ML/AI systems in fast-paced environments.
• Strong production MLOps experience including data pipelines, model versioning, deployment automation, and monitoring.
• Experience taking ML models from prototype to production and maintaining them long-term.

Responsibilities

• Own production hardening and deployment of ML prototype applications for manufacturing operations.
• Serve as on-call owner for deployed ML systems and build end-to-end ML platform capabilities across cloud and OT environments.
• Interface with data engineering 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.
• Design training infrastructure that supports fast iteration while ensuring reproducibility and governance.

Skills

Python

PyTorch

TensorFlow

Docker

Kubernetes

MLOps

On-call rotations

Education

Bachelor's degree in Computer Science or related field

Tools

Flyte

Airflow

Kubeflow

Temporal

MLflow

Job description

Anduril Industries builds digital systems that directly power real-world shipbuilding and manufacturing operations. In the Maritime Digital Production (MDP) team, you will help take machine learning from prototype to reliable production systems across cloud and on-premises OT environments. When systems go down, production stops, so your work will matter on factory floors and in the people who operate them.

Location: Santa Ana, CA (onsite)
Compensation: USD 220,000 - 292,000 per year

What you’ll do

You will own production hardening and deployment of ML prototype applications for manufacturing operations, bridging ML research with production-grade systems that can run continuously. You will also serve as the on-call owner for deployed ML systems and build end-to-end ML platform capabilities across cloud and factory-floor OT environments.

• Own production hardening and deployment of ML prototype applications, turning research concepts into reliable, maintainable systems
• Participate in on-call rotations for deployed ML products running on factory floors, supporting high availability and rapid incident response
• Build and maintain inference infrastructure across cloud environments and on-premises OT hardware on the factory floor
• Deploy and operate ML systems in factory environments, including edge compute clusters and OT networks, with reliability and observability in mind
• 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 data engineering 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
• Translate ML engineering requirements into infrastructure for datasets, training pipelines, and inference systems
• Partner with manufacturing engineers and factory operators to understand production workflows and shape MLOps requirements
• Drive make/buy decisions by evaluating internal and vendor capabilities for Applied Intelligence needs
• Architect and own the end-to-end ML platform stack, from data ingestion and labeling through training orchestration, model serving, and lifecycle management
• Build model-serving and inference frameworks for real-time and batch execution across cloud, edge, and shop-floor systems
• Implement event-driven data pipelines and telemetry systems that feed 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 that supports fast iteration while maintaining 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
• 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
• Leverage AI tooling (coding assistants, automation) and contribute to team engineering practices
• Mentor junior engineers and data scientists on production ML best practices

What you bring

• 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 ability to take ML models from prototype to production and maintain them long-term
• Experience with on-call rotations and production incident management for data or ML systems
• Strong Python skills and deep learning framework experience (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
• Ability to build 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 is required because this role needs access to export controlled data

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 that powers Anduril's shipbuilding factories, including data infrastructure, manufacturing execution system (ArsenalOS), scheduling engine, and AI systems.

MDP operates between Operational Technology and Information Technology, where factory-floor machines, edge compute, and OT networks meet 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 scales them across multiple sites. MDP is scaling from a founding team to 70+ engineers across multiple U.S. sites.

Tools you may work with

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

Benefits

Anduril provides a comprehensive, competitive benefits package (available at little to no cost to employees), including health, recovery, and whatever comes next (details referenced via “Explore Our Benefits”).

Security and privacy

• No financial requests: Anduril will never solicit payment or demand personal financial details during the hiring process.
• Verify communications: recruiter emails will come only from an @anduril.com address.
• Verify agency authenticity: reach out to [e-mail].
• Report suspicious outreach: contact [e-mail].

To view the candidate data privacy policy, 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 assess potential risks.