Senior Machine Learning Engineer (Applied Intelligence)

Anduril-1; Santa Ana (CA); On-site; USD 220,000 - 292,000; Full time

Benefits offered by this job

Equity grants

Competitive benefits

Job summary

Anduril Industries is seeking a Machine Learning Engineer to bridge ML research and production, owning infrastructure that brings prototypes to reliable systems on factory floors. You will translate ML requirements into scalable infrastructure, coordinate with data engineering, manufacturing operations, and enterprise teams, and ensure 24/7 reliability in real environments.

You will lead the end-to-end ML platform development, including data pipelines, training orchestration, and model serving

Qualifications

• 6+ years of software/ML engineering experience
• Strong production MLOps experience: pipelines, versioning, deployment, monitoring
• Proven ability to take ML models from prototype to production
• Experience with on-call rotations and production incident management
• Proficiency in Python; experience with PyTorch/TF
• Experience building containerized ML services (Docker, Kubernetes)
• Solid data engineering and time-series/event-driven data handling
• Experience with observability for model performance and data drift
• Excellent cross-team technical communication
• Ability to operate in high-availability, low-latency environments
• Coordinate across multiple teams to translate requirements into solutions
• Degree in CS/Engineering or equivalent
• U.S. Person status required

Responsibilities

• Own production hardening and deployment of ML apps
• Participate in on-call rotations for deployed ML products
• Build and maintain inference infrastructure across cloud and on-premise OT
• Deploy and operate ML systems in factory environments including edge clusters
• Implement monitoring, alerting, and drift detection for production ML
• Define rollback procedures and safety reviews for AI systems
• Interface with data engineering to define ML data infrastructure needs
• Coordinate with enterprise teams to secure compute resources and data access
• Translate ML requirements into infrastructure for datasets, training pipelines, and inference systems
• Drive make/buy decisions based on needs
• Architect end-to-end ML platform from data ingestion to model lifecycle
• Support real-time and batch model serving across environments
• Design training infrastructure enabling reproducibility and governance
• Own dataset creation, curation, and versioning
• Implement model governance, retraining triggers, and artifact management
• Write production-quality code with tests, participate in reviews
• Mentor junior engineers and data scientists

Skills

Python

Deep learning (PyTorch/TF)

MLOps

Docker & Kubernetes

On-call incident management

Data engineering

Model governance

Technical communication

Education

BS in Computer Science / Engineering

Tools

Docker

Kubernetes

Flyte/Airflow/Kubeflow

Job description

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 Machine Learning Engineer on the Applied Intelligence initiative, you will be the critical bridge between ML research and production operations. You will own the infrastructure that brings ML prototypes from concept to reliable, production-grade systems running on factory floors and serving manufacturing teams.

This role requires someone who can translate ML requirements into production infrastructure, coordinate across teams (data engineering, Big Anduril, manufacturing operations), and serve as the on-call owner for deployed ML systems. You'll be responsible for ensuring that AI applications don't just work in demos—they work 24/7 in real manufacturing environments where downtime has real consequences.

WHAT YOU'LL DO

Infrastructure Ownership & Production Operations

• Own production hardening and deployment of ML prototype applications, transforming research concepts into reliable, maintainable production systems
• Take part in on-call rotations for deployed ML products running on factory floors and serving Mantech, ensuring 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 comprehensive monitoring, alerting, and drift detection systems to maintain operational SLOs for production ML applications
• Define and execute rollback procedures, validation gates, and safety reviews for AI systems in production

Cross-Functional Coordination & Requirements Translation

• Interface directly 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 needed for ML initiatives
• Execute the technical vision of ML engineers by translating their requirements into concrete infrastructure for datasets, training pipelines, and inference systems
• Partner with manufacturing engineers and factory operators to understand production workflows and translate them into MLOps requirements
• Drive make/buy decisions by evaluating internal and vendor capabilities against Applied Intelligence needs

Platform & Infrastructure Development

• Architect and own the end-to-end ML platform stack—from data ingestion and labeling through training orchestration to model serving and lifecycle management
• Build model-serving and inference frameworks optimized for production environments, supporting 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 including feature stores, vector databases, model registries, orchestration layers, and observability systems
• Design training infrastructure that enables ML engineers to iterate quickly while maintaining reproducibility and governance

Data & Model Lifecycle Management

• Own dataset creation, curation, versioning, and management processes that enable high-quality model development
• Implement model governance processes 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

Code Quality & Team Practices

• 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) in your development workflow and contribute to team 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: data pipeline management, model versioning, deployment automation, and production monitoring
• Proven track record of 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 proficiency in Python 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 with ability to work across ML research, data engineering, and operations teams
• Experience building systems that must operate reliably under real-world operational 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 as this position needs to access export controlled data

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 (e.g., working with 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, with demonstrated success 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

US Salary Range

$220,000 - $292,000 USD

Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package.

Additionally, Anduril offers top-tier benefits for full-time employees, including:

At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next.

Protecting Yourself from Recruitment Scams

Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information.

To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind:

No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates.

Please always verify communications:

• Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address.
• Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to [e-mail].

Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links.

What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to [e-mail]. Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts.

Export Controls

This position requires access to information and technology that is subject to U.S. export controls.

Equal Employment Opportunity

As set forth in Anduril Industries’s Equal Employment Opportunity policy