This role pays more than
83%
of similar roles. Most pay
$165,000–$245,000
— the shaded band above.
At the midpoint, this role pays about
$256k
versus about
$205k
for comparable roles.
Based on 240 similar postings.
Employer
About Anduril Industries
Anduril Industries is a defense technology company that builds advanced hardware and software systems for national security, including autonomous drones, surveillance systems, and the Lattice AI command platform.
Anduril Industries currently has
1697 open roles
on FindRole.
Listed pay typically runs
$146,000–$194,000
across 1504 roles with salary data.
As a Staff AI Infrastructure Engineer on the Air Dominance & Strike team, you will architect, build, and scale an end-to-end machine learning platform powering autonomous systems. You will own the technical roadmap for ML infrastructure, creating MLOps tooling to train, evaluate, host, and serve complex models including LLMs, computer vision, and RL agents across cloud and air-gapped tactical edge networks. Your daily work involves building high-performance ETL pipelines for multi-modal sensor data, developing automated tools for hyperparameter tuning, and establishing CI/CD pipelines with robust validation. You will utilize Python, Go, or C++ while leveraging Docker, Kubernetes, PyTorch Distributed, Ray, and Slurm to manage GPU scheduling and distributed training. This role solves the challenge of managing large-scale model lifecycles and data processing for autonomous aerial and multi-domain robotic systems in high-stakes environments.
Architect and scale the end-to-end machine learning platform for autonomous systems.
Build infrastructure for training, orchestrating, and experimenting with LLMs, computer vision, and RL agents.
Develop high-performance ETL pipelines to process terabytes of multi-modal sensor and simulation data.
Create model serving frameworks optimized for both cloud environments and air-gapped tactical edge networks.
Establish CI/CD pipelines for ML models featuring automated validation and canary deployments.
Build automated pipelines for continuous evaluation, model validation, and reinforcement learning alignment loops.
Identify and eliminate bottlenecks in the machine learning research lifecycle through automated tools.
Define technical roadmaps and mentor engineers to expand the infrastructure engineering team.
What we're looking for
7+ years of software engineering experience in designing and operating production-scale MLOps systems.
Proficiency in Python, Go, C++, or similar backend languages with deep knowledge of ML system design and distributed computing.
Experience with containerized deployments (Docker, Kubernetes), GPU orchestration, and distributed training frameworks like PyTorch Distributed or Ray.
Hands-on experience building distributed ETL pipelines for large-scale multi-modal sensor data.
Proven ability to set technical direction, lead complex migrations, and mentor senior engineers.
Eligible to obtain and maintain an active U.S. Top Secret security clearance.
Experience with air-gapped or highly regulated environments like IL5/IL6 (preferred).
Experience with LLMs, Generative AI, Reinforcement Learning pipelines, or next-generation AI accelerators (preferred).