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. 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: Air Dominance & Strike designs, builds, and flies autonomous air vehicles—from collaborative combat aircraft to expendable cruise missiles and counter-UAS interceptors. Our vehicles move from whiteboard to first flight on timelines that traditional primes consider impossible, which means our design cycles live or die on how fast we can close the iteration loop. The Anduril AI Engineering team exists to collapse that loop. We are engineers first. We work from engineering first principles and unlock capability through machine learning and AI. We are building to scale across CFD, FEA, thermal, and electromagnetics, with pipelines, architectures, and validation practices that carry across programs. About the Job We are looking for a Machine Learning Engineer to apply the latest research in physics ML to the toughest bottlenecks in our design cycle. This role owns the entire surrogate modeling stack for Air Dominance & Strike—the architectures, the training infrastructure, the simulation data pipelines that feed it, and the tooling design engineers use to consume predictions. You will develop, train, and deploy surrogate models that accelerate the physics simulations underpinning our air vehicle programs. Working alongside aerodynamicists, structures engineers, and thermal engineers, your models will directly inform decisions on hardware that actually flies. Where current methods fall short, you will develop new ones, with ample room to identify novel applications of physics ML across our portfolio. Defense experience is not required. We are looking for engineers who came to machine learning through the complex physical problems they were already trying to solve. This role is based onsite in our Costa Mesa, CA office. What You'll Do Own the Surrogate Modeling Stack: Drive the end-to-end design, training, and deployment of production-grade surrogate models to accelerate critical simulation workflows (CFD, FEA, thermal, structural, and aeroelastic) across air vehicle design. Develop State-of-the-Art Architectures: Design and implement neural architectures tailored to engineering physics, developing new techniques for uncertainty quantification, active learning, and inverse problems (such as geometry and shape optimization). Build Robust Data & Training Infrastructure: Create the pipelines behind the training—extracting, aggregating, and sanitizing tens of thousands of high-fidelity results from solver outputs. Optimize & Integrate: Optimize inference for the design loop (maximizing GPU utilization, batched evaluation, and interactive-speed latency) and seamlessly integrate surrogate predictions into the tooling our domain engineers already use. Collaborate & Mentor: Partner with domain engineers to identify where ML delivers the highest leverage, stay current with Physics AI research, and provide technical mentorship to non ML engineers. Qualifications Education: BS, MS, or PhD in aerospace, thermal, mechanical, or electrical engineering, or in machine learning/AI/data science with a demonstrated engineering foundation. Experience: 3+ years of experience taking ML models from R&D into production using large-scale scientific or engineering datasets. Physics ML Expertise: Working knowledge of modern surrogate architectures (e.g. GNNs, Transolver, DoMINO & GeoTransolver) combined with hands-on experience running physical simulations (CFD, FEA, thermal, etc.) and a command of the underlying numerical methods. Software & Frameworks: Proficiency in Python and MATLAB; experience with PyTorch, TensorFlow, and NVIDIA PhysicsNeMo (Modulus); and experience developing on Linux with GPU accelerators and distributed training. Data & Engineering Best Practices: Track record of building production data pipelines from heterogeneous engineering sources, utilizing uncertainty quantification, conducting statistical analysis, and building data science dashboards Clearance: Must be a U.S. Person eligible to obtain and maintain a U.S. Top Secret security clearance Preferred Qualifications Advanced Physics ML: Graduate research focused on AI for scientific simulation, experience solving inverse problems (geometry optimization/design under uncertainty), and hands-on experience building active learning or adaptive sampling pipelines. Domain Expertise: Prior work in aerospace, automotive, turbomachinery, or another simulation-heavy hardware domain, with familiarity in commercial solvers, meshing tools, and CAD interoperability. Advanced Tooling: Working knowledge of foundational ML methods (Gaussian processes, XGBoost, Elastic Net regression & clustering) with the ability to build custom architectures, advanced skills in visualization software (Plotly, Seaborn, Matplotlib), and ML Ops orchestration experience (e.g. Docker, Weights & Biases, AWS S3, Lambda & SageMaker) Per Anduril's expansion plans, we anticipate this job will be based out of Long Beach, CA beginning late 2027. 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

Defense experience is not required. We are looking for engineers who came to machine learning through the complex physical problems they were already trying to solve.
Build Robust Data & Training Infrastructure: Create the pipelines behind the training—extracting, aggregating, and sanitizing tens of thousands of high-fidelity results from solver outputs.
Collaborate & Mentor: Partner with domain engineers to identify where ML delivers the highest leverage, stay current with Physics AI research, and provide technical mentorship to non ML engineers.
Education: BS, MS, or PhD in aerospace, thermal, mechanical, or electrical engineering, or in machine learning/AI/data science with a demonstrated engineering foundation.
Experience: 3+ years of experience taking ML models from R&D into production using large-scale scientific or engineering datasets.
Software & Frameworks: Proficiency in Python and MATLAB; experience with PyTorch, TensorFlow, and NVIDIA PhysicsNeMo (Modulus); and experience developing on Linux with GPU accelerators and distributed training.
Clearance: Must be a U.S. Person eligible to obtain and maintain a 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

Domain Expertise: Prior work in aerospace, automotive, turbomachinery, or another simulation-heavy hardware domain, with familiarity in commercial solvers, meshing tools, and CAD interoperability.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlpythondockeraws

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Vision

Pto