This role pays more than
84%
of similar roles. Most pay
$202,800–$254,750
— the shaded band above.
At the midpoint, this role pays about
$256k
versus about
$229k
for comparable roles.
Based on 240 similar postings.
Employer
About Anduril Industries
Anduril Industries is a defense technology company that builds advanced hardware and software systems for national security, including autonomous drones, surveillance systems, and the Lattice AI command platform.
Anduril Industries currently has
1697 open roles
on FindRole.
Listed pay typically runs
$146,000–$194,000
across 1504 roles with salary data.
Senior ML Engineer, Core Development joins the Air Dominance & Strike team to accelerate the design cycle for autonomous air vehicles through physics-based machine learning. This role owns the entire surrogate modeling stack, where you will develop, train, and deploy models to speed up critical simulations including CFD, FEA, thermal, structural, and aeroelastic workflows. You will build robust data pipelines from solver outputs, design neural architectures for uncertainty quantification and inverse problems, and optimize inference for production-grade tools. The role requires proficiency in Python, MATLAB, PyTorch, TensorFlow, and NVIDIA PhysicsNeMo on Linux with GPU accelerators. Candidates should possess expertise in GNNs, Transolver, or DoMINO, alongside experience with large-scale engineering datasets. This position solves complex physical bottlenecks in aerospace design by replacing traditional simulation methods with advanced machine learning architectures to inform hardware decisions.
Own the end-to-end design, training, and deployment of production-grade surrogate models for air vehicle simulations.
Develop neural architectures tailored to engineering physics including uncertainty quantification and inverse problems.
Build data pipelines to extract, aggregate, and sanitize high-fidelity results from solver outputs.
Optimize inference for the design loop by maximizing GPU utilization and ensuring interactive-speed latency.
Integrate surrogate predictions into existing tools used by domain engineers.
Identify high-leverage areas where machine learning can accelerate engineering workflows.
Provide technical mentorship to non-ML engineers regarding physics-based AI research.
What we're looking for
Must be a U.S. Person eligible to obtain and maintain a U.S. Top Secret security clearance.
Bachelor's, Master's, or PhD in aerospace, thermal, mechanical, electrical engineering, or machine learning/AI/data science with an engineering foundation.
3+ years of experience taking ML models into production using large-scale scientific or engineering datasets.
Working knowledge of modern surrogate architectures and hands-on experience running physical simulations like CFD, FEA, and thermal.
Proficiency in Python and MATLAB, with experience in PyTorch, TensorFlow, NVIDIA PhysicsNeMo, and Linux with GPU accelerators.
Track record of building production data pipelines from heterogeneous engineering sources including uncertainty quantification and statistical analysis.
Graduate research in AI for scientific simulation or experience solving inverse problems (preferred).
Experience in aerospace/automotive domains, advanced ML methods like Gaussian processes, and MLOps orchestration tools (preferred).
Machine Learning
Deep Learning
Generative AI
Diffusion Models
Large Language Models (LLMs)
Vision-Language Models (VLMs)
World Foundation Models
Synthetic Data
Model Compression
Quantization
CUDA
Triton
Isaac Sim
Computer Vision