Senior ML Engineer, Core Development

Anduril; Costa Mesa (CA); On-site; USD 140,000 - 200,000; Full time

Job summary

Anduril Industries is seeking a Machine Learning Engineer to own the surrogate modeling stack for Air Dominance & Strike programs. You will design, train, and deploy models that accelerate CFD, FEA, and aeroelastic simulations, collaborating with aero, structures, and thermal engineers.

You will drive end-to-end ML production, from data pipelines to deployment, exploring novel physics ML techniques and contributing to fast, reliable engineering decisions.

Qualifications

• BS, MS, or PhD in aerospace, thermal, mechanical, or electrical engineering, or ML/AI with engineering foundation.

Responsibilities

• Own the surrogate modeling stack for accelerating physics simulations in air vehicle design.
• Develop state-of-the-art neural architectures for engineering physics and uncertainty quantification.

Skills

Python

ML in production

Physics ML

GNNs

Surrogate modeling

Education

BS/MS/PhD in engineering or ML/AI

Tools

MATLAB

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 MATLA