Senior ML Engineer, Core Development

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.

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).