Senior Machine Learning Engineer (Applied Intelligence)

Santa Ana · Santa Ana, CA (OC-42) · USD 220,000-292,000 · 6+ years experience · Full-time · Posted Oct 15, 2026

At a glance

As a Machine Learning Engineer on the Applied Intelligence initiative at Anduril Industries, 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.
Working at the intersection of data engineering, MLOps, and production operations, you'll execute the vision of ML engineers by building robust datasets, training infrastructure, and inference systems.

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

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
U.S. Person status is required as this position needs to access export controlled data

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
Eligible to obtain and maintain a U.S. Secret security clearance

Highly competitive equity grants included in the majority of full time offers
Top-tier benefits for full-time employees supporting health, recovery, and whatever comes next

Show all 15 skills