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Senior Machine Learning Engineer (Applied Intelligence)

Anduril· Maritime & Maneuver Dominance : Heavy Metal - Engineering & Operations : Software Engineering
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Santa Ana, CA$220k - $292kFull-timePosted 6d ago

About the role

Machine Learning Engineer bridging ML research and production, owning infrastructure to bring ML prototypes to reliable production systems on factory floors, with on-call ownership and cross-functional collaboration across data engineering, manufacturing operations, and enterprise teams.

Responsibilities

  • 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
  • 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
  • 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
  • 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
  • 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

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

Skills

Required:PythonDeep learning frameworksDockerKubernetesMLOpsData engineeringObservabilityOn-call / incident managementCross-functional collaboration

Benefits

  • Comprehensive, competitive benefits package (health, recovery, and related coverages) available at little to no cost to employees
Last updated Oct 21, 2026. Interested? Apply on the company site.

About Anduril

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