Senior Machine Learning Engineer (Applied Intelligence)

Anduril • Santa Ana, CA

4d

About The Position

The Senior Machine Learning Engineer will serve as the bridge between ML research and production operations for Anduril’s Applied Intelligence initiative. The role owns infrastructure for bringing ML prototypes into reliable, production-grade systems used on factory floors and by manufacturing teams. It combines data engineering, MLOps, and production operations, translating ML requirements into datasets, training infrastructure, and inference systems that operate in cloud and on-premises OT environments.

The engineer will help ensure AI applications operate reliably around the clock in real manufacturing environments. This includes production hardening and deployment, on-call ownership, operational monitoring, incident response, and coordination with data engineering, enterprise, manufacturing engineering, and factory operations teams. The role also owns the end-to-end ML platform stack and dataset and model lifecycle processes, while contributing production-quality code, engineering practices, and mentorship.

Requirements

• At least 6 years of experience in software engineering or ML engineering roles building production systems.
• Strong production MLOps experience, including data-pipeline management, model versioning, deployment automation, and production monitoring.
• 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 such as PyTorch or 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 and ability to work across ML research, data engineering, and operations teams.
• Experience building systems that operate reliably under real-world constraints, including high availability, low latency, or constrained environments.
• Ability to coordinate across multiple teams and translate requirements into technical solutions.
• Degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
• U.S. Person status is required because the position needs access to export-controlled data.

Nice To Haves

• Experience in manufacturing, industrial, or OT-adjacent domains such as MES, SCADA, factory automation, or IoT.
• Background deploying ML models to edge devices or on-premises hardware in production environments.
• Experience with workflow or orchestration tools such as Flyte, Airflow, Kubeflow, or Temporal.
• Familiarity with GPU infrastructure management and inference optimization, including TensorRT or Triton Inference Server.
• Experience with MLOps tools for experiment tracking, such as MLflow or Weights & Biases; feature stores, such as Feast or Tecton; and model registries.
• Experience building RAG systems, using vector databases such as Pinecone, Weaviate, or Milvus, or building LLM deployment pipelines.
• Background deploying computer vision models, OCR/IDP systems, or document-processing pipelines.
• Experience coordinating with platform or infrastructure teams at larger organizations, such as central ML platform teams.
• Familiarity with event-driven architectures, IoT/UNS patterns, and real-time systems integration.
• Knowledge of edge ML deployment, model optimization such as quantization or pruning, or deploying models on resource-constrained devices.
• Experience in regulated environments such as NNPI or ITAR and with secure model and 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.

Responsibilities

• Own production hardening and deployment of ML prototype applications, turning research concepts into reliable, maintainable production systems.
• Participate in on-call rotations for deployed ML products and provide rapid incident response.
• Build and maintain inference infrastructure across cloud environments and on-premises OT hardware.
• 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 production AI systems.
• Work with data engineering to define and deliver ML-specific data infrastructure needs.
• Coordinate with enterprise teams to secure compute resources, labeling infrastructure, data access, and modeling capabilities.
• Translate ML engineers’ technical vision into infrastructure for datasets, training pipelines, and inference systems.
• Partner with manufacturing engineers and factory operators to understand workflows and translate them into MLOps requirements.
• Evaluate internal and vendor capabilities to drive make-or-buy decisions.
• 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 production, supporting real-time and batch execution across cloud, edge, and shop-floor systems.
• Implement event-driven data pipelines and telemetry systems that provide 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 reproducible and governed training infrastructure that enables ML engineers to iterate quickly.
• Own dataset creation, curation, versioning, and management processes for model development.
• Implement model governance processes for validation, traceability, retraining triggers, and version control.
• Build data-labeling workflows, quality assurance, and continuous data-pipeline monitoring systems.
• Manage model versioning, experiment tracking, and artifact management across development and production environments.
• Write production-quality code with comprehensive tests and participate in code reviews and architectural discussions.
• Establish best practices for MLOps, observability, data management, and secure handling of sensitive production data.
• Use AI tooling in development workflows and contribute to team engineering practices.
• Mentor junior engineers and data scientists on production ML practices.

Benefits

• Highly competitive equity grants are included in the majority of full-time offers.
• Top-tier benefits package described as comprehensive and competitive, available at little to no cost to employees.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior