# Senior ML Engineer, Core Development

**Company**: Anduril Industries
**Location**: Costa Mesa, California
**Work arrangement**: onsite
**Experience**: senior
**Job type**: full-time
**Salary**: $220,000-$292,000 USD
**Category**: Engineering
**Industry**: Technology

**Apply**: https://job-boards.greenhouse.io/andurilindustries/jobs/5216691007?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_a3ddb1b8-baa

## Description

Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology.

We are looking for a Machine Learning Engineer to apply the latest research in physics ML to the toughest bottlenecks in our design cycle.

## 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 (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.

## 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.

## Benefits

Anduril offers top-tier benefits for full-time employees, including highly competitive equity grants, comprehensive health benefits, and other perks.

## Skills

### Required
- Python
- MATLAB
- PyTorch
- TensorFlow
- NVIDIA PhysicsNeMo (Modulus)
- Linux
- GPU accelerators
- distributed training
- machine learning
- AI
- data science
- physics ML
- surrogate architectures
- GNNs
- Transolver
- DoMINO & GeoTransolver
- CFD
- FEA
- thermal simulations

### Nice to have
- Advanced Physics ML
- graduate research focused on AI for scientific simulation
- experience solving inverse problems (geometry optimization/design under uncertainty)
- hands-on experience building active learning or adaptive sampling pipelines
- domain expertise in aerospace, automotive, turbomachinery, or another simulation-heavy hardware domain
- familiarity in commercial solvers, meshing tools, and CAD interoperability
- foundational ML methods (Gaussian processes, XGBoost, Elastic Net regression & clustering)
- advanced skills in visualization software (Plotly, Seaborn, Matplotlib)
- ML Ops orchestration experience (e.g. Docker, Weights & Biases, AWS S3, Lambda & SageMaker)

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