# Senior Machine Learning Engineer (Applied Intelligence) **Company**: Anduril Industries **Location**: Santa Ana, California **Experience**: senior **Job type**: full-time **Salary**: $220,000-$292,000 USD **Category**: Engineering **Industry**: Technology **Apply**: https://job-boards.greenhouse.io/andurilindustries/jobs/5250787007?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_437601f2-38e ## Description Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. Maritime Digital Production (MDP) is the software and digital systems function within Anduril's Heavy Metal division. As a Machine Learning Engineer on the Applied Intelligence initiative, you will be the critical bridge between ML research and production operations. ## Responsibilities ### 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 ## Skills ### Required - Python - deep learning frameworks (PyTorch, TensorFlow) - containerized ML services using Docker and Kubernetes - data engineering skills including pipeline orchestration, data quality management - observability for model performance, inference accuracy, and data drift - strong technical communication skills ### Nice to have - 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 --- Source: [Apply at job-boards.greenhouse.io](https://job-boards.greenhouse.io/andurilindustries/jobs/5250787007?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)