Anduril
Senior ML Engineer, Core Development
$220K–$292K
Costa Mesa, California, United States · Senior · Full-time · Posted Aug 26, 2026
- Experience
- 3+ years; defense experience is not required.
- Education
- BS, MS, or PhD in aerospace, thermal, mechanical, or electrical engineering, or in machine learning/AI/data science with a demonstrated engineering foundation.
- Visa sponsorship
- Must be a U.S. Person eligible to obtain and maintain a U.S. Top Secret security clearance.
About the job
Own and deliver the surrogate modeling stack that uses physics-based machine learning to accelerate simulations and engineering decisions for autonomous air vehicles.
What you'll do
- Design, train, and deploy surrogate models and architectures for engineering simulations, including uncertainty quantification and inverse problems.
- Build training-data pipelines and infrastructure from high-fidelity solver outputs; optimize inference and integrate predictions into design tools.
- Collaborate with domain engineers to identify high-impact ML applications and provide technical mentorship.
What they ask for
- 3+ years taking ML models from R&D into production using large-scale scientific or engineering datasets.
- Working knowledge of modern surrogate architectures, hands-on physical simulation experience, and command of underlying numerical methods.
- Proficiency in Python and MATLAB; experience with PyTorch, TensorFlow, and NVIDIA PhysicsNeMo (Modulus), plus Linux, GPU accelerators, and distributed training.
- Must be a U.S. Person eligible to obtain and maintain a U.S. Top Secret security clearance.
Nice to have
- Graduate research in AI for scientific simulation, inverse problems, or active learning/adaptive sampling.
- Experience in a simulation-heavy hardware domain and familiarity with solvers, meshing, CAD interoperability, advanced visualization, or ML Ops orchestration.
Pay
$220,000 — $292,000 USD (base salary)
Tools
Python, MATLAB, PyTorch, TensorFlow, NVIDIA PhysicsNeMo (Modulus), Linux, GPU accelerators, distributed training
AI focus
Develop and deploy physics ML surrogate models for engineering simulations, including uncertainty quantification, active learning, and inverse problems such as geometry and shape optimization.
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