Forward Deployed Engineering Intern - Simulations & Physics AI

We’re hiring Forward Deployed Engineer Interns for the summer of 2026. As part of our internship program, you’ll have the opportunity to contribute directly to real-world projects that redefine design workflows — accelerating design cycles, cutting prototyping costs, and enabling engineers to use AI natively in their workflows. You’ll help build the future of engineering design, leveraging GPU-native solvers, physics-driven AI models, and scalable cloud infrastructure.

The ideal candidate for this role has a background in engineering (bonus points for aerospace or mechanical engineering) and has practical experience with scientific computing, data science and deep learning workflows. 

We offer a competitive stipend, mentorship from experienced engineers and researchers, and immerse you in a fast-paced environment where innovation isn’t optional — it’s necessary. 

What You'll Do

• Pioneer Physics AI adoption by creating and applying  PhysicsAI models for real world design optimization problems using Luminary Cloud’s scalable data-generation platform
• Own the entire PhysicsAI model pipeline of data generation, model training and evaluation, and embedding in real-world use cases.
• Collaborate with cross-functional teams across engineering, product, sales and marketing, communicating your work to different stakeholders and directly shaping the next generation of Physics AI.

Basic Qualifications

• Bachelors/Masters in Engineering, Computational Science, Physics or Applied Mathematics
• Experience with using physics simulation tools and workflows: geometry creation, meshing, simulation and post processing
• Experience with data science and deep learning workflows
• Proficiency in Python scripting and Linux systems
• Self-starter mentality with the ability to work independently and drive results.
• Exceptional communication skills, particularly when engaging with technical audiences.

Preferred Qualifications

• Previous experience with Physics AI / scientific machine learning in applied or research settings
• Experience using at least one deep learning library (e.g. PyTorch/Tensorflow/JAX )
• Experience with CAD tools (eg. SolidWorks, Onshape etc.)
• Experience using SDK APIs
• Experience with cloud/high-performance computing environments (GCP/AWS/Azure)