Staff AI Infrastructure Engineer

ABOUT THE TEAM

The Air Dominance & Strike team at Anduril develops aerial and multi-domain robotic systems. The teamis responsible fortaking products like Fury (unmanned fighter jet) and Barracuda (air-breathing cruise missile) from concept to product. The team also develops Lattice for Mission Autonomy Andurils premier software platform that enables masses of Fury Barracuda and other first andthird partyrobots to collaborate across various missions. We work in close coordination with specialist teams like Perception Motion Planning Hardware and Test Engineering to solve some of the hardest problems facing our customers. We are looking for software engineers and roboticists excited about creating a powerful autonomy software stack that includes computer vision motion planning SLAM controls estimation and secure communications.

ABOUT THE JOB

We are looking for a foundingStaff AI Infrastructure Engineerto architect build and scale the end-to-end machine learning platform that powers Andurils autonomous systems.

As a Staff Engineer you will own the technical roadmap for our ML platform. You will build the robust infrastructureMLOpstooling and systems architecturerequiredto train evaluate host and serve complex AI models (including LLMs computer vision and RL agents) in both cloud environments and air-gapped offline tactical edge networks. You will be a force multiplier for our AI Research Scientistsoptimizingtheir experimentation velocity and managing the lifecycle of terabytes of multi-modal sensor and simulation data. Over time you will help recruit mentor and expand this infrastructure engineering team.

WHAT YOULL DO

• Design build andmaintainour foundational training orchestration and experimentation infrastructure to supportstate-of-the-artmodel development.
• Actively identify measure and eliminate bottlenecks in the ML research lifecycle. Build highly automated tools for hyperparameter tuning model profiling and experimentation tracking.
• Design and scale robust high-performance ETL pipelines capable of processing terabytes of multi-modal data (video camera feeds radar flight telemetry and simulation logs) captured from physical assets and test sites.
• Architect high-throughput low-latency model serving frameworks optimized for both scalable cloud environments and air-gapped resource-constrained tactical edge environments. Build CI/CD pipelines for ML models with automated validation canary deployments and rollback capabilities.
• Build robust automated pipelines for continuous evaluation model validation and reinforcement learning alignment loops (RLHF/DPO) to guarantee model safety and predictability in high-stakes environments.
• Work closely with AI Researchers Computer Vision teams and platform engineers to design unified infrastructure standards across the companys autonomous systems programs.

REQUIRED QUALIFICATIONS

• 7 years of software engineering experience with a proventrack recordof 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.

PREFERRED QUALIFICATIONS

• 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.

The salary range for this role is an estimate based on a wide range of compensation factors inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience education and/or training critical skills and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Andurils total compensation package. Additionally Anduril offers top-tier benefits for full-time employees including:

At Anduril we invest in our people. Our comprehensive competitive benefits package (available at little to no cost to employees) ensures youre supported in health recovery and whatever comes next.For more information Explore Our Benefits.

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