Funding (reported): Seed round(s) with reported totals including $8.5M and company materials referencing $18.5M

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 the speed 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."