Staff AI Infrastructure Engineer – ML Platform & Edge

Anduril Industries; Costa Mesa (CA); On-site; USD 220,000 - 292,000; Full time

Job summary

Anduril Industries seeks a founding Staff AI Infrastructure Engineer to architect and scale the ML platform powering its autonomous systems. You will own the technical roadmap, build robust ML infra, MLOps tooling, and data pipelines for training, evaluation, and serving complex AI models in cloud and air-gapped edge networks.

You will enable researchers, mentor an infra team, and drive secure, scalable ML deployments across multi-domain assets.

Qualifications

• 7+ years of software engineering experience designing, building, and operating production-scale ML systems and platforms (MLOps).
• Proficient in Python, Go, C++, or similar backend languages with distributed computing knowledge.
• Deep experience with containerized deployments (Docker, Kubernetes) and GPU scheduling/orchestration.

Responsibilities

• Design, build, and maintain foundational training, orchestration, and experimentation infrastructure.
• Identify and eliminate bottlenecks in the ML lifecycle and build automated tools for tuning and tracking.
• Scale ETL pipelines for terabytes of multi-modal data from assets and test sites.
• Architect high-throughput model serving for cloud and edge environments with CI/CD.
• Build pipelines for evaluation, validation, and RLHF/DPO to ensure safety.
• Collaborate with AI researchers and platform engineers to standardize infrastructure.

Skills

Python

Go

C++

ML Systems

Distributed Systems

Tools

Docker

Kubernetes

PyTorch Distributed

Ray

Slurm

Megatron-LM

Job description

Anduril Industries seeks a founding Staff AI Infrastructure Engineer to architect and scale the ML platform powering its autonomous systems. You will own the technical roadmap, build robust ML infra, MLOps tooling, and data pipelines for training, evaluation, and serving complex AI models in cloud and air-gapped edge networks.

You will enable researchers, mentor an infra team, and drive secure, scalable ML deployments across multi-domain assets.