Surrogate Modeling ML Engineer – Aerospace Simulations

Anduril Industries; Costa Mesa (CA); On-site; USD 220,000 - 292,000; Full time

Benefits offered by this job

Equity grants

Competitive benefits package

Job summary

Anduril Industries is seeking a Machine Learning Engineer to apply physics-informed ML to accelerate aerospace design cycles. You will own the surrogate modeling stack—from architecture to training, deployment, and integration with engineering tools.

You will collaborate with aerodynamicists, structures, and thermal engineers to accelerate simulations and influence hardware decisions. Defense experience is not required, and the role is onsite in Costa Mesa, CA.

Qualifications

• Education: BS/MS/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.

Responsibilities

• Own the Surrogate Modeling Stack: Drive the end-to-end design, training, and deployment of production-grade surrogate models to accelerate critical simulation workflows 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 (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.

Skills

ML in production

Physics-informed ML

Collaboration & mentorship

Security clearance readiness

Education

BS/MS/PhD in engineering or ML with engineering foundation

Tools

Python

MATLAB

PyTorch

TensorFlow

NVIDIA Modulus

Linux with GPUs

Job description

Anduril Industries is seeking a Machine Learning Engineer to apply physics-informed ML to accelerate aerospace design cycles. You will own the surrogate modeling stack—from architecture to training, deployment, and integration with engineering tools.

You will collaborate with aerodynamicists, structures, and thermal engineers to accelerate simulations and influence hardware decisions. Defense experience is not required, and the role is onsite in Costa Mesa, CA.