Senior AI Infrastructure Engineer
Costa Mesa · Seattle · Washington · Boston, MA (BOS-01) · USD 191,000-253,000 · 5+ years experience · Full-time · Posted Sep 24, 2026
At a glance
- Experience
- 5+ years experience
- Employment
- Full-time
- Location
- Costa Mesa · Seattle · Washington · Boston, MA (BOS-01)
- Salary
- USD 191,000-253,000 · Base salary
About the role
- The Senior AI Infrastructure Engineer will build, scale, and optimize the end-to-end machine learning platform powering Anduril’s autonomous systems.
- This role involves owning critical components of the ML platform and MLOps tooling, building and operating infrastructure to train, evaluate, host, and serve complex AI models across cloud environments and air-gapped, edge-deployed networks.
Responsibilities
- Build, optimize, and maintain scalable training, orchestration, and experimentation infrastructure to accelerate state-of-the-art model development.
- Identify and resolve bottlenecks in the ML lifecycle by developing tooling for experiment tracking, automated profiling, and hyperparameter tuning.
- Implement and scale robust data pipelines (ETL) to process multi-modal data captured from physical assets and test sites.
- Deploy high-throughput, low-latency model serving frameworks optimized for both cloud environments and resource-constrained, air-gapped tactical edge hardware.
- Develop robust CI/CD pipelines for ML models, including automated regression testing, validation benchmarks, and safe rollout/rollback strategies.
- Implement pipelines for model evaluation, validation, and reinforcement learning alignment loops (RLHF/DPO) to ensure predictability and safety in mission-critical deployments.
- Partner with AI Researchers and Computer Vision engineers to translate modeling requirements into scalable, reusable infrastructure.
- Mentor peers, conduct thorough design and code reviews, and champion engineering best practices across the team.
Requirements
- 5+ years of software engineering experience with demonstrated success in building and operating production-scale machine learning infrastructure or distributed systems.
- Proficiency in Python, Go, or C++, with a strong grasp of software engineering fundamentals, systems design, and concurrent programming.
- Hands-on experience with container orchestration (Docker, Kubernetes) and distributed training frameworks (e.g., PyTorch Distributed, Ray, Slurm, or Megatron-LM).
- Experience building and maintaining distributed data pipelines handling large-scale unstructured or multi-modal datasets.
- Track record of owning projects end-to-end—from technical design to production deployment and operational monitoring.
- Eligible to obtain and maintain an active U.S. Top Secret security clearance.
Preferred qualifications
- Experience deploying ML infrastructure, model serving, or artifacts in secure, air-gapped, or regulated environments (e.g., IL5/IL6, GovCloud).
- Hands-on experience profiling GPU/accelerator workloads, resolving hardware/network bottlenecks, and optimizing compute utilization.
- Experience supporting workloads for Large Language Models, Generative AI, or Reinforcement Learning (RL) pipelines.
- Experience with multi-tenant cluster management, including fair scheduling, GPU slicing, and quota enforcement.
- Familiarity with production ML observability frameworks, including data drift detection and automated evaluation pipelines.
Benefits
- Competitive equity grants included in the majority of full-time offers
- Comprehensive, competitive benefits package available at little to no cost supporting health, recovery, and more
Skills
- Python
- Go
- C++
- Docker
- Kubernetes
- PyTorch
- Ray
- Slurm
- Megatron-LM
- LLMs
- Generative AI
- Reinforcement Learning
