Staff AI Infrastructure Engineer

The role

You will architect, build, and scale an end-to-end machine learning platform. This includes designing infrastructure, MLOps tooling, and systems architecture for training and serving AI models in cloud and air-gapped environments.

What you will do

• Design, build, and maintain foundational training, orchestration, and experimentation infrastructure.
• Identify, measure, and eliminate bottlenecks in the ML research lifecycle.
• Build automated tools for hyperparameter tuning, model profiling, and experimentation tracking.
• Design and scale high-performance ETL pipelines for multi-modal data.
• Architect high-throughput, low-latency model serving frameworks for cloud and tactical edge environments.
• Build CI/CD pipelines for ML models with automated validation, canary deployments, and rollback capabilities.
• Build automated pipelines for continuous evaluation, model validation, and reinforcement learning alignment loops.
• Work with AI Researchers, Computer Vision teams, and platform engineers to design unified infrastructure standards.
• Own the technical roadmap for the ML platform.
• Recruit, mentor, and expand the infrastructure engineering team.

Required qualifications

• 7+ 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).
• Experience setting technical direction, leading complex system migrations, and mentoring senior engineers.
• Eligible to obtain and maintain an active U.S. Top Secret security clearance.

Nice to have

• Experience building and running ML infrastructure, model serving, or software registries within secure, air-gapped, or highly regulated environments (e.g., IL5/IL6, GovCloud).
• Experience specifically building training and evaluation platforms for Large Language Models, Generative AI architectures, or Reinforcement Learning (RL) pipelines.
• Experience profiling training hardware performance, identifying bottlenecks across networks and memory, and optimizing hardware utilization.
• Experience designing and operating multi-tenant ML platforms that serve multiple research teams, with robust resource isolation, quota management, and fair scheduling across shared GPU clusters.
• Hands-on experience with next-generation AI accelerators beyond standard GPUs (e.g., AWS Trainium, Google TPUs, or custom ASICs) for training and inference workloads.
• Experience building production monitoring and observability systems for ML models, including prediction drift detection, data quality monitoring, and automated retraining triggers.

Benefits

• Comprehensive, competitive benefits package available at little to no cost to employees.

Before you apply

• Eligible to obtain and maintain an active U.S. Top Secret security clearance.
• Consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening.

About the company

Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. The company's family of systems is powered by Lattice OS, an AI-powered operating system.