Staff AI Infrastructure Engineer
$220k - $292k • 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 seeking a founding Staff AI Infrastructure Engineer to architect, build, and scale the end-to-end machine learning platform that powers the company's autonomous systems. This role will own the technical roadmap for the ML platform, building robust infrastructure, MLOps tooling, and systems architecture to train, evaluate, host, and serve complex AI models. The engineer will optimize experimentation velocity for AI Research Scientists, manage large datasets, and contribute to recruiting and mentoring the infrastructure engineering team.
Responsibilities
- Design, build, and maintain foundational training, orchestration, and experimentation infrastructure for state-of-the-art model development.
- Identify, measure, and eliminate bottlenecks in the ML research lifecycle, building automated tools for hyperparameter tuning, model profiling, and experimentation tracking.
- Design and scale robust, high-performance ETL pipelines for processing terabytes of multi-modal data.
- Architect high-throughput, low-latency model serving frameworks for cloud and edge environments, building CI/CD pipelines for ML models.
- Build automated pipelines for continuous evaluation, model validation, and reinforcement learning alignment loops (RLHF/DPO).
- Collaborate with AI Researchers, Computer Vision teams, and platform engineers to design unified infrastructure standards.
Requirements
- 7+ years of software engineering experience.
- Proven track record of building and scaling ML platforms.
- Experience with MLOps tooling and systems architecture.
- Experience training, evaluating, hosting, and serving complex AI models, including LLMs, computer vision, and RL agents.
- Experience with cloud environments and air-gapped, offline tactical edge networks.
- Experience managing terabytes of multi-modal sensor and simulation data.
- Experience with CI/CD pipelines for ML models.
- Experience with automated validation, canary deployments, and rollback capabilities.
- Experience with continuous evaluation, model validation, and reinforcement learning alignment loops (RLHF/DPO).
- Experience working with AI Researchers and Computer Vision teams.