Senior ML Engineer, Core Development

Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing theexpertise technology and business model of the 21st centurys most innovative companies to the defense industry Anduril is changing how military systems are designedbuiltrealtime 3Dcommandand control center. As the world enters an era of strategic competition Anduril is committed to bringingcutting-edgeautonomy 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 vehiclesfrom collaborative combataircraftto 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 AIEngineering team exists to collapse that loop.

We are engineers first. We work from engineering first principles and unlock capability through machine learningand 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 physicsML to the toughest bottlenecks in our design cycle. This role owns the entire surrogate modeling stack for Air Dominance & Strikethe 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 thatactually flies. Where current methods fall short you will develop new ones with ample room toidentifynovel applications of physicsML across our portfolio.

Defense experience is notrequired. We are looking for engineers who came to machine learning through the complex physical problems they were already trying to solve.

This role is basedonsitein our Costa Mesa CA office.

What Youll 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 trainingextracting aggregating and sanitizing tens of thousands of high-fidelity results from solver outputs.
• Optimize & Integrate:Optimizeinference for the design loop (maximizing GPUutilization 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 toidentifywhere ML delivers the highest leverage stay current withPhysicsAI research and provide technical mentorship tonon MLengineers.

Qualifications

• Education:BS MS or PhD in aerospacethermalmechanical 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.
• PhysicsML Expertise:Working knowledge of modern surrogate architectures (&GeoTransolver) combined with hands-on experience running physical simulations (CFD FEA thermal etc.) and a command of the underlying numerical methods.
• Software & Frameworks:Proficiencyin Python and MATLAB; experience withPyTorch TensorFlowand NVIDIAPhysicsNeMo(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 sourcesutilizinguncertainty quantification conducting statistical analysis and building data science dashboards
• Clearance:Must be a U.S. Person eligible to obtain andmaintaina security clearance

Preferred Qualifications

• Advanced PhysicsML: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 processesXGBoostElasticNetregression& clustering)with the ability to build custom architectures advanced skills in visualization software (Plotly Seaborn Matplotlib) and ML Opsorchestrationexperience ( Weights & Biases AWSS3 Lambda&SageMaker)

Per Andurils expansion plans we anticipate this job will be based out of Long Beach CA beginning late 2027.

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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