Founding AI + CFD Researcher

📍 San Francisco | Work Directly with CEO & founding team | Report to CEO | OpenAI for Physics | 🏢 5 Days Onsite

Location: Onsite in San Francisco

Compensation: Competitive Salary + Equity

UniversalAGI is building OpenAI for Physics. AI startup based in San Francisco and backed by Elad Gil (#1 Solo VC), Eric Schmidt (former Google CEO), Prith Banerjee (ANSYS CTO), Ion Stoica (Databricks Founder), Jared Kushner (former Senior Advisor to the President), David Patterson (Turing Award Winner), and Luis Videgaray (former Foreign and Finance Minister of Mexico). We’re building foundation AI models for physics that enable end-to-end industrial automation from initial design through optimization, validation, and production.

We're building a high-velocity team of relentless researchers and engineers that will define the next generation of AI for industrial engineering. If you're passionate about AI, physics, or the future of industrial innovation, we want to hear from you.

As a founding AI + CFD Researcher, you'll be in the arena from day one, at the exact intersection where deep learning meets computational physics. This is your chance to build foundation AI models that don't just automate CFD, but fundamentally reimagine how physics simulation works.

You'll work directly with the CEO and founding team to tackle research problems that have never been solved before: training AI to understand fluid dynamics, turbulence, and mesh quality the way an expert engineer does. You're not just applying ML to physics, you're inventing new architectures, loss functions, and training paradigms specifically designed for the complexities of CFD.

• Develop novel AI architectures for physics simulation: neural operators, graph neural networks, transformers, diffusion models, surrogate models or whatever works best for learning fluid dynamics

• Design and implement training pipelines that can ingest massive CFD datasets and learn to predict flow fields, optimize meshes, or generate designs with accuracy that matches or exceeds traditional numerical solvers

• Bridge physics and ML deeply: Ensure our models respect physical constraints, conservation laws, and numerical stability, embedding your CFD expertise directly into model architecture and loss functions

• Run large-scale experiments on simulation data, iterate rapidly on model performance, and drive our research roadmap based on what actually works

• Work hands-on with CFD tools (OpenFOAM, Ansys, STAR-CCM+) to generate training data, validate model outputs, and understand where traditional simulation struggles

• Collaborate directly with domain experts and customers in automotive, aerospace, and other industries to understand their workflows, pain points, and validation criteria

• Publish and present breakthrough results, internally and externally, as we push the boundaries of what's possible in AI for physics

• Move fast and ship: Take research from idea to production-ready model in weeks, not months, and see your work deployed to real customers

This is a role for someone who speaks both languages fluently, CFD and deep learning, and is ready to solve some of humanity's hardest problems at their intersection.

• 2+ years of hands-on experience building and training deep learning models for scientific computing, physics simulation, or related domains (GNNs, GCNNs, Transformers, Vision Models, Neural Operators, PINNs)

• Strong foundation in CFD: Deep understanding of fluid mechanics, numerical methods, mesh generation, boundary conditions, and solver frameworks

• Proven ML research ability: Track record of implementing novel architectures, running large-scale experiments, and iterating quickly based on results

• Expert-level coding skills in Python and deep learning frameworks (PyTorch, JAX, TensorFlow)

• Experience with CFD software (OpenFOAM, Ansys Fluent, STAR-CCM+, or similar) and the ability to generate, process, and analyze simulation data programmatically

• Strong communicator capable of bridging customers, engineers, and researchers, translating between physics intuition and ML architecture decisions

• Outstanding execution velocity: Ships fast, iterates rapidly, and thrives in ambiguity

• Exceptional creativity and problem-solving ability: Willing to try unconventional approaches when standard methods fail

• Comfortable in high-intensity startup environments with evolving priorities and tight deadlines

• PhD in Machine Learning, Aerospace, Computational Physics, Applied Math, or related field with focus on physics-informed neural networks, graph neural networks, transformers, geometric convolutional neural networks, neural operators, or scientific ML

• Published research in top-tier ML or computational physics venues (NeurIPS, ICML, ICLR, JCP, JFM, etc.)

• Experience with neural operators (FNO, DeepONet, UNet, Transformers, etc.) or graph neural networks for physical systems

• Domain expertise in automotive aerodynamics, aerospace, or other CFD-heavy industries

• Large-scale distributed training experience with multi-GPU or multi-node setups

• Experience at high-growth AI startups (Seed to Series C) or leading research labs

• Open-source contributions to ML or CFD codebases

• Forward deployed experience working directly with customers to solve their hardest problems

• Technical Respect: Ability to earn respect through hands-on technical contribution

• Intensity: Thrives in our unusually intense culture - willing to grind when needed

• Customer Obsession: Passionate about solving real customer problems, not just cool tech

• Deep Work: Values long, uninterrupted periods of focused work over meetings

• High Availability: Ready to be deeply involved whenever critical issues arise

• Communication: Can translate complex technical concepts to customers and team

• Growth Mindset: Embraces the compounding returns of intelligence and continuous learning

• Startup Mindset: Comfortable with ambiguity, rapid change, and wearing multiple hats

• Work Ethic: Willing to put in the extra hours when needed to hit critical milestones

• Team Player: Collaborative approach with low ego and high accountability

• Opportunity to shape the technical foundation of a rapidly growing foundational AI company.

• Work on cutting-edge industrial AI problems with immediate real-world impact.

• Direct collaboration with the founder & CEO and ability to influence company strategy

• Competitive compensation with significant equity upside.

• In-person first culture - 5 days a week in office with a team that values face-to-face collaboration.

• Access to world-class investors and advisors in the AI space.

We provide great benefits, including:

• Competitive compensation and equity.

• Competitive health, dental, vision benefits paid by the company.

• 401(k) plan offering.

• Flexible vacation.

• Team Building & Fun Activities.

• Great scope, ownership and impact.

• AI tools stipend.

• Monthly commute stipend.

• Monthly wellness / fitness stipend.

• Daily office lunch & dinner covered by the company.

• Immigration support.

“The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood; who strives valiantly; who errs, who comes short again and again... who at the best knows in the end the triumph of high achievement, and who at the worst, if he fails, at least fails while daring greatly." - Teddy Roosevelt

At our core, we believe in being “in the arena.” We are builders, problem solvers, and risk-takers who show up every day ready to put in the work: to sweat, to struggle, and to push past our limits. We know that real progress comes with missteps, iteration, and resilience. We embrace that journey fully knowing that daring greatly is the only way to create something truly meaningful.

If you're ready to join the future of physics simulation, push creative boundaries, and deliver impact, UniversalAGI is the place for you.