Machine Learning Engineer
Core
Design and train deep learning models for physics simulation across aerodynamic and engineering domains to replace traditional simulation methods.
Role type
Machine Learning Engineer (Generative Physics)
Builds
Generative Physics simulation platform for automotive, aerospace, and energy design optimization
Domain
Physics simulation, aerodynamics, computational fluid dynamics (CFD)
Deliverable
production ML models
Required skills
deep learning model development, optimization, generalization, Python, TensorFlow/Pytorch/JAX, geometry representation, model architecture design, production pipeline integration
Preferred skills
aerodynamics/CFD expertise, design optimization algorithms, physics-informed machine learning
Technologies
TensorFlow, PyTorch, JAX
Responsibilities
Design and train deep learning models for physics simulation; Drive optimization efforts for model inference speed and accuracy; Research effective ways to represent geometric design variations; Partner with engineering teams to deploy and monitor models in production-grade pipelines; Contribute to design decisions around model and data architecture
Seniority
Mid-to-Senior, hands-on IC