Job description

Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years. ABOUT THE TEAM The Air Dominance & Strike team at Anduril develops aerial and multi-domain robotic systems. The team is responsible for taking products like Fury (unmanned fighter jet) and Barracuda (air-breathing cruise missile) from concept to product. The team also develops Lattice for Mission Autonomy, Anduril’s premier software platform that enables masses of Fury, Barracuda, and other first and third party robots 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 founding Staff AI Infrastructure Engineer to architect, build, and scale the end-to-end machine learning platform that powers Anduril’s autonomous systems. As a Staff Engineer, you will own the technical roadmap for our ML platform. You will build the robust infrastructure, MLOps tooling, and systems architecture required to 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 Scientists, optimizing their 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 YOU’LL DO Design, build, and maintain our foundational training, orchestration, and experimentation infrastructure to support state-of-the-art model 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 company's autonomous systems programs. REQUIRED QUALIFICATIONS 7+ years of software engineering experience with a proven track record of 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. US Salary Range $220,000 — $292,000 USD 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 Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including: Benefits At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next. For more information, Explore Our Benefits . Protecting Yourself from Recruitment Scams Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidates. We've observed a rise in sophisticated phishing and fraudulent schemes where individuals impersonate Anduril representatives, luring job seekers with false interviews or job offers. These scammers often attempt to extract payment or sensitive personal information. To ensure your safety and help you navigate your job search with confidence, please keep the following critical points in mind: No Financial Requests: Anduril will never solicit payment or demand personal financial details (such as banking information, credit card numbers, or social security numbers) at any stage of our hiring process. Our legitimate recruitment is entirely free for candidates. Please always verify communications: Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address. Via Agency Partner: If contacted by a recruiting agency for an Anduril role, their email will clearly identify their agency. If you suspect any suspicious activity, please verify the agency's authenticity by reaching out to contact@anduril.com . Exercise Caution with Unsolicited Outreach: If you receive any communication that appears suspicious, contains grammatical errors, or makes unusual requests, do not engage. Always confirm the sender's email domain is @anduril.com before providing any personal information or clicking on links. What to Do If You Suspect Fraud: Should you encounter any questionable or fraudulent outreach claiming to be from Anduril, please report it immediately to contact@anduril.com . Your proactive caution is invaluable in protecting your personal information and upholding the security and trustworthiness of our recruitment efforts. Data Privacy To view Anduril's candidate data privacy policy, please visit anduril.com . By submitting your application, you consent to Anduril Industries using a third-party service provider to conduct pre-employment risk, integrity, and due diligence screening and assessing potential risks as part of your application process. This third-party service provider provides risk-intelligence services that may include analysis of sanctions and watchlists, adverse media, public-record information, and other lawful open-source or commercial data sources. This third-party service provider does not act as a consumer reporting agency. Use of this provider helps to ensure compliance with applicable laws and protect technology, intellectual property, and organizational security.

Requirements

Must-haves

We are looking for a founding Staff AI Infrastructure Engineer to architect, build, and scale the end-to-end machine learning platform that powers Anduril’s autonomous systems.
Design, build, and maintain our foundational training, orchestration, and experimentation infrastructure to support state-of-the-art model development.
Actively identify, measure, and eliminate bottlenecks in the ML research lifecycle. Build highly automated tools for hyperparameter tuning, model profiling, and experimentation tracking.
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 company's autonomous systems programs.
7+ years of software engineering experience with a proven track record of 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.
Direct from Anduril: If you receive an email from one of our recruiters, it will only come from an @anduril.com address.

Nice-to-haves

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.

Application prep

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Keywords to mirror

aimachine learningmlci/cdpythongodockerkubernetesaws

Platform: greenhouse
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Benefits

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