Staff AI Infrastructure EngineerActive$292K

Anduril Industries
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The opportunity

Anduril Industries is a defense technology company with a mission to transform U. S.

What you'll do

  • Design, build, and maintain our foundational training, orchestration, and: experimentation infrastructure to support state-of-the-art model development.

  • Actively identify, measure, and eliminate bottlenecks in the ML research: lifecycle. Build highly automated tools for hyperparameter tuning, model profiling, and experimentation tracking.

  • Design and scale robust, high-performance ETL pipelines capable of processing: terabytes of multi-modal data (video, camera feeds, radar, flight telemetry, and simulation logs) captured from physical assets and test sites.

  • Architect high-throughput, low-latency model serving frameworks optimized for: both scalable cloud environments and air-gapped, resource-constrained tactical edge environments. Build CI/CD pipelines for ML models with automated validation, canary deployments, and rollback capabilities.

  • Build robust, automated pipelines for continuous evaluation, model: validation, and reinforcement learning alignment loops (RLHF/DPO) to guarantee model safety and predictability in high-stakes environments.

  • Work closely with AI Researchers, Computer Vision teams, and platform: engineers to design unified infrastructure standards across the company's autonomous systems programs.

What they're looking for

  • + years of software engineering experience with a proven track record of: designing, building, and operating production-scale machine learning systems and platforms (MLOps).
  • Proficient in Python, Go, C++, or similar backend languages. Deep: understanding of ML systems design, memory management, and distributed computing.
  • Deep experience with containerized deployments (Docker, Kubernetes), GPU: scheduling/orchestration, and distributed training frameworks (e.g., PyTorch Distributed, Ray, Slurm, or Megatron-LM).
  • Hands-on experience building distributed data pipelines (ETL) and managing: massive datasets (terabytes of unstructured/multi-modal sensor data).