About the role
Machine Learning Engineer BeyondMath is a pioneering startup, backed by top-tier VCs, on a mission to reshape the frontiers of engineering through Foundational AI models for Physics. We are replacing traditional, slow and expensive simulation methods with AI that rivals accuracy at orders of magnitude higher speed. We are moving beyond the "generic AI" hype to solve the world’s hardest physical engineering challenges in automotive, aerospace, and energy.
The Role As a Machine Learning Engineer, you’ll play a central role in advancing our Generative Physics simulation platform. You’ll work at the intersection of ML research and engineering contributing to core model development, shaping model architecture, and delivering performant systems that integrate seamlessly into our real-world design optimization workflows. You'll work closely with our ML research team, software engineers, and industry partners to deploy robust, scalable models that deliver real-world impact.
Responsibilities Physics-Focused AI Model Development: Design and train deep learning models for physics simulation across aerodynamic and engineering domains. Scalability & Performance: Drive optimization efforts for model inference speed, accuracy, and robustness on large-scale industrial datasets. Geometry Representation : Research effective ways to represent geometric design variations for efficient use by machine learning models. Production Integration: Partner with engineering teams to deploy and monitor models in production-grade pipelines and tools. Architecture & Design: Contribute to design decisions around model and data architecture, tooling, and ML infrastructure. Essential
Requirements Industrial Experience: Strong track record applying ML to complex real-world problems (ideally including geometry or physical systems). Foundational Knowledge : Deep understanding of machine learning theory, including optimization, generalisation, and various model architectures.● Programming : Strong python skills and experience with deep learning libraries (TensorFlow/PyTorch/JAX). Communication : Ability to clearly explain complex ML concepts and research findings to both technical and non-technical audiences. Education : Master's Degree (PhD preferred) in Machine Learning, Computer Science, or a related quantitative field. Highly Desirable Aerodynamics/CFD Expertise: Familiarity with aerodynamic principles and computational fluid dynamics is a major plus. Design Optimization: Prior experience in optimization algorithms, particularly inthe context of engineering design. Physics/Science ML: Experience integrating physical laws or constraints intomachine learning models.