Founding Deep Learning 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

Universal

AGI 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 Deep Learning Researcher, you'll be architecting and training the foundation models that will transform how industries approach physics simulation and engineering design. This isn't a research role in isolation—you'll be shipping models that customers depend on for critical engineering decisions worth millions of dollars.

You’ll work directly with the CEO and founding team to push the boundaries of what AI can do with physics data. You'll design novel architectures that can learn from CFD simulations, build training pipelines that scale to petabytes of data, and iterate rapidly based on customer feedback and real-world performance.

This is your opportunity to define how foundation models learn physics, from the ground up.

• Design and train foundation models for physics simulation, working with GNNs, CNNs, GCNs, Point Net, RegDGCNN, Neural Operators, transformer architectures, diffusion models, and other cutting‑edge approaches adapted for physical systems
• Build training pipelines from scratch: data preprocessing, tokenization strategies for physics data, loss functions that capture physical accuracy, and training loops that scale to massive datasets
• Optimize model architectures for physics:
  Balance model capacity, inference speed, and accuracy for industrial use cases with strict performance requirements
• Develop novel approaches to physics‑informed learning:
  Integrate physical constraints, conservation laws, and domain knowledge directly into model architectures and training objectives
• Fine‑tune and adapt models to customer‑specific domains, data, and requirements while maintaining generalization and avoiding catastrophic forgetting
• Collaborate with infrastructure team to optimize training efficiency, implement distributed training strategies, and ensure models can be served at scale
• Validate model performance against ground truth simulations and real‑world engineering data, building robust evaluation frameworks that customers trust
• Work directly with customers to understand their physics problems, gather domain expertise, and translate engineering requirements into model capabilities
• Drive rapid experimentation
  :
  Run dozens of training experiments per week, systematically testing hypotheses and improving model performance
• Ship models to production
  :
  Take responsibility for model quality from initial training through deployment and ongoing monitoring in customer environments

This is a role for someone who lives at the intersection of deep learning research and production ML, who can both read the latest papers and ship models that work reliably in high‑stakes industrial settings.

• 3+ years of hands‑on experience training deep learning models, with a track record of shipping models to production
• Deep expertise in modern deep learning frameworks (PyTorch, JAX) and model architectures (Transformers, Diffusion Models, Graph Neural Networks, GNNs, CNNs, GCNs, Point Net, RegDGCNN, Neural Operators, etc.)
• Strong foundation in distributed training
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  Experience with multi‑GPU and multi‑node training, gradient accumulation, mixed precision, and optimization techniques
• Expert‑level Python and…