🕒 5 days ago
Research and develop generative image and video models for autonomous driving, advancing temporal consistency, multiview coherence, and controllable scene generation from research through real-world integration.
AI Researcher
📅 Mar 18
Physics-based Deep LearningMachine Learning Architecture DesignPartial Differential EquationsPytorch
📜 Description
- Develop machine learning architectures for solving complex partial differential equations, including aerodynamic simulations.
- Turn mathematical research into high-performance code and integrate models into production design platforms.
- Develop efficient deep learning representations for complex geometric design variations.
- Mentor researchers and engineers and define research standards, reproducibility pipelines, and HPC requirements.
- Publish research at leading conferences and represent the work in the global AI community.
- Collaborate with CFD specialists and software engineers to maintain physical constraints and model performance.
🛠️ Requirements
- PhD or MSc in computer science, physics, mathematics, or a related quantitative field.
- 5+ years of postgraduate AI/ML research experience, including taking models from research into production.
- Expert proficiency in PyTorch, JAX, or TensorFlow, including custom layers, loss functions, and optimization loops.
- Strong publication record at leading AI or physics-focused venues.
- Experience with scalable training infrastructure, distributed GPU training, and data pipeline automation.
- Experience with PINNs, operator learning, or equivariant neural networks is desirable.
- Familiarity with aerodynamics, fluid dynamics, or structural mechanics is desirable.
- Familiarity with C++ and CUDA for low-level model optimization is desirable.
Full job description
Senior AI Researcher
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. We are looking for a Senior AI Researcher who balances scientific curiosity with the engineering discipline required to see models thrive in production environments.
The Role
As a Senior AI Researcher, you will be a core architect of our technical roadmap. This is not a "siloed" research role; you will lead the transition from theoretical breakthroughs in Physics based Deep Learning to robust, scalable systems used by world-class engineers.
You will have the creative freedom to set research agendas while ensuring our models remain grounded in physical reality and industrial-scale performance.
Key Responsibilities
Architect Physics-AI Foundations: Lead the research and development of novel ML architectures (e.g., Transformers, GNNs, or Diffusion models) designed specifically to solve complex partial differential equations (PDEs) including aerodynamic simulations.
Bridge Research & Production: Translate high-level mathematical concepts into clean, high-performance code. You won’t just "throw models over the wall"; you will ensure they are optimized for inference and integrated into our production design platform.
Advance Geometry Representation: Pioneer new ways to represent complex geometric design variations for efficient use in deep learning models.
Strategic Leadership: Mentor junior researchers and engineers. Help define our internal research standards, reproducibility pipelines, and high-performance compute (HPC) infrastructure requirements.
External Impact: Represent BeyondMath in the global AI community. Publish influential research at top-tier conferences (NeurIPS, ICML, ICLR) and position the company as the leader in "AI for Physics."
Cross-Functional Collaboration: Partner with CFD specialists and software engineers to ensure our models respect physical constraints while maintaining thespeed advantages of neural networks.
About You
You are a rare hybrid: a scientist who loves the elegance of a theorem, but an engineer who gets a thrill from seeing a model successfully optimize a real-world turbine or airframe. You thrive in the ambiguity of a "greenfield" opportunity and have the grit to solve problems where no textbook solution exists.
Essential Requirements:
PhD or MSc in Computer Science, Physics, Mathematics, or a related quantitative field.
5+ years of post-grad experience in AI/ML research, with a demonstrable track record of models made it from the lab into production environments.
Deep Technical Mastery: Expert-level proficiency in PyTorch, JAX, or TensorFlow, with a focus on building custom layers, loss functions, and optimization loops.
Published Excellence: A strong record of high-quality publications in top-tier venues (e.g., NeurIPS, ICML, CVPR, or physics-specific AI journals).
Systems Thinking: Experience with scalable training infrastructure, including distributed training across GPU clusters and data pipeline automation.
Highly Desirable:
Physics-ML Expertise: Experience with Physics-Informed Neural Networks (PINNs), Operator Learning (DeepONet/FNO), or Equivariant Neural Networks.
Domain Knowledge: Familiarity with Aerodynamics, Fluid Dynamics, or Structural Mechanics.
Engineering Rigor: Familiarity with C++, CUDA for low-level model optimization.
Why Join Us?
Full Ownership: You will have a direct seat at the table in shaping the future of a company redefining an entire industry.
High Impact: Your work will directly accelerate the transition to sustainable energy and more efficient transport.
Elite Team: Work alongside veterans from world-leading AI labs and engineering firms in a culture of "impact with integrity."
We receive a large number of applications and take the time to carefully review each one. While this means our response time might be a bit slower, we deeply appreciate your patience
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