This role pays more than
71%
of similar roles. Most pay
$146,125–$227,750
— the shaded band above.
At the midpoint, this role pays about
$222k
versus about
$187k
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
850 open roles
on FindRole.
Listed pay typically runs
$166,000–$220,000
across 821 roles with salary data.
As a Senior AI Infrastructure Engineer on the Air Dominance & Strike team, you will build, scale, and optimize the end-to-end machine learning platform powering autonomous systems like unmanned fighter jets and cruise missiles. You will own critical components of the MLOps tooling, developing infrastructure to train, evaluate, host, and serve complex models including LLMs, computer vision, and RL agents across cloud and air-gapped edge networks. Your daily work involves building scalable training pipelines, managing multi-modal data ETL processes for video and telemetry, and implementing CI/CD workflows for safety-critical deployments. You will utilize Python, Go, or C++ alongside tools like Docker, Kubernetes, PyTorch Distributed, Ray, and Slurm. The role focuses on solving the technical challenge of deploying high-throughput, low-latency model serving in resource-constrained environments to ensure reliable performance in mission-critical defense operations.
Build and maintain scalable training, orchestration, and experimentation infrastructure for machine learning models.
Develop tools for experiment tracking, automated profiling, and hyperparameter tuning to resolve lifecycle bottlenecks.
Implement robust ETL pipelines to process multi-modal data from physical assets and test sites.
Deploy high-throughput, low-latency model serving frameworks for cloud and air-gapped tactical edge hardware.
Create CI/CD pipelines for ML models including automated regression testing and safe rollout strategies.
Develop evaluation and reinforcement learning alignment loops to ensure safety in mission-critical deployments.
Translate research requirements into scalable infrastructure while mentoring peers and conducting code reviews.
What we're looking for
5+ years of software engineering experience in building and operating production-scale machine learning infrastructure or distributed systems.
Proficiency in Python, Go, or C++ with strong knowledge of systems design and concurrent programming.
Hands-on experience with container orchestration (Docker, Kubernetes) and distributed training frameworks like PyTorch Distributed, Ray, Slurm, or Megatron-LM.
Experience building and maintaining distributed data pipelines for large-scale unstructured or multi-modal datasets.
Proven track record of owning projects from technical design through production deployment and operational monitoring.
Eligible to obtain and maintain an active U.S. Top Secret security clearance.
Experience deploying ML infrastructure in secure, air-gapped, or regulated environments (preferred).
Experience with GPU profiling, multi-tenant cluster management, or supporting LLM and Reinforcement Learning pipelines (preferred).
OCI
GPU
RoCE
Infiniband
Distributed Systems
Linux
Python
Java
C
C++
GoLang
Shell Scripting
SQL
MySQL
Redis
Memcache
Infrastructure as Code
Telemetry
Load Testing