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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior ML Engineer, Core Development - **Company:** Anduril Industries - **Location:** Costa Mesa, CA, United States - **Experience:** Expert - **Salary:** $220,000.0 - $292,000.0 - **Contract:** Permanent contract - **Skills:** Computer-Aided Design, Artificial Intelligence, Amazon S3, Computational Fluid Dynamics, Cluster Analysis, Linux, Distributed Computing Environment, Python (Programming Language), MATLAB, Machine Learning, Operational Databases, Tensorflow, User-Centered Design, Pytorch, Core Api, Gaussian, Matplotlib, Xgboost, Plotly, Machine Learning Operations, Data Pipelines, Docker - **Published:** August 28, 2026 - **Apply:** https://www.clearancejobs.com/jobs/9124223/senior-ml-engineer-core-development ## About the Role * 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, * 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) ## Description 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., 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. 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