Senior AI Infrastructure Engineer
$191k - $253k • Costa Mesa, California, United States; Seattle, Washington, United States; Washington, District of Columbia, United States
Posted 12d ago
Job Location
Costa Mesa, California, United States; Seattle, Washington, United States; Washington, District of Columbia, United States
Tech Stack
Remote Work Policy
On-site
Categories
AI Infrastructure Engineer
About the job
Anduril Industries is a defense technology company focused on transforming military capabilities with advanced technology. We are seeking a Senior AI Infrastructure Engineer to build, scale, and optimize the end-to-end machine learning platform that powers our autonomous systems. In this role, you will own critical components of our ML platform and MLOps tooling, building and operating the infrastructure required to train, evaluate, host, and serve complex AI models across various environments. You will collaborate closely with AI Research Scientists and Platform Engineers to streamline model development, enhance hardware utilization, and ensure the reliable deployment of models into safety-critical operational settings.
Responsibilities
- Build, optimize, and maintain scalable training, orchestration, and experimentation infrastructure for model development.
- Identify and resolve ML lifecycle bottlenecks by developing tooling for experiment tracking, automated profiling, and hyperparameter tuning.
- Implement and scale distributed data pipelines (ETL) for multi-modal data processing.
- Deploy high-throughput, low-latency model serving frameworks for cloud and edge environments.
- Develop robust CI/CD pipelines for ML models, including automated testing and rollout strategies.
- Implement pipelines for model evaluation, validation, and reinforcement learning alignment loops.
- Translate modeling requirements into scalable, reusable infrastructure in partnership with AI Researchers and Computer Vision engineers.
- Mentor peers, conduct design and code reviews, and champion engineering best practices.
Requirements
- 5+ years of software engineering experience in building and operating production-scale machine learning infrastructure or distributed systems.
- Proficiency in Python, Go, or C++, with strong software engineering fundamentals, systems design, and concurrent programming skills.
- Hands-on experience with container orchestration (Docker, Kubernetes) and distributed training frameworks (e.g., PyTorch Distributed, Ray, Slurm, or Megatron-LM).
- Experience building and maintaining distributed data pipelines for large-scale unstructured or multi-modal datasets.