Senior Machine Learning Engineer (Applied Intelligence)Active$292K
The opportunity
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…
What you'll do
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
What they're looking for
- 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)
