below marketmost similar roles pay hereabove market
This role pays more than
84%
of similar roles. Most pay
$174,600–$246,000
— the blue band above.
At the midpoint, this role pays about
$256k
versus about
$210k
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
906 open roles
on FindRole.
Listed pay typically runs
$166,000–$220,000
across 860 roles with salary data.
The Staff AI Infrastructure Engineer joins the Air Dominance & Strike team to architect and scale the end-to-end machine learning platform powering autonomous systems. This role involves owning the technical roadmap for MLOps tooling, building robust infrastructure to train, evaluate, host, and serve complex AI models like LLMs and RL agents, and managing terabytes of multi-modal sensor data. You will design high-performance ETL pipelines, automated hyperparameter tuning tools, and low-latency model serving frameworks for both cloud and air-gapped tactical edge networks. Required skills include proficiency in Python, Go, or C++, along with experience in Docker, Kubernetes, PyTorch Distributed, Ray, and Slurm. The work focuses on solving complex autonomy problems involving computer vision, motion planning, and secure communications for aerial and multi-domain robotic systems.
Design and maintain foundational training, orchestration, and experimentation infrastructure for state-of-the-art model development.
Build automated tools for hyperparameter tuning, model profiling, and experimentation tracking to eliminate research bottlenecks.
Design and scale high-performance ETL pipelines to process terabytes of multi-modal sensor and simulation data.
Architect high-throughput, low-latency model serving frameworks for cloud and air-gapped tactical edge environments.
Build CI/CD pipelines for ML models featuring automated validation, canary deployments, and rollback capabilities.
Develop automated pipelines for continuous evaluation, model validation, and reinforcement learning alignment loops.
Establish unified infrastructure standards across autonomous systems programs in coordination with AI Research and Computer Vision teams.
Own the technical roadmap for the machine learning platform and mentor the infrastructure engineering team.
What we're looking for
7+ years of software engineering experience designing and operating production-scale machine learning systems and platforms.
Proficient in Python, Go, C++, or similar backend languages with deep understanding of ML systems design and distributed computing.
Deep experience with containerized deployments (Docker, Kubernetes), GPU scheduling, and distributed training frameworks like PyTorch Distributed or Ray.
Hands-on experience building distributed ETL data pipelines and managing massive multi-modal sensor datasets.
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.
Experience building ML infrastructure or model serving within secure, air-gapped, or highly regulated environments (preferred).
Experience building training and evaluation platforms for LLMs, Generative AI, or Reinforcement Learning pipelines (preferred).