Generative AI Engineer - PHD

About this role

Overview:

Recruiting for an SF-based deep-tech startup building foundation ML models for multi-physics simulation. It’s a small, highly technical team — including multiple professors from top colleges like Berkeley, multiple PhDs, and even a Nobel Prize winner. Well funded, hybrid environment — actively hiring both researchers and engineers who want real ownership over foundational work and system-level impact.

By developing fast, generalizable, and accurate solvers, we aim to make inverse design and design space exploration accessible and ubiquitous — unlocking orders-of-magnitude improvements in how physical systems are engineered. Our focus is on generalizable, production-grade foundation models, not one-off surrogates.

The Role

We are seeking a highly technical Generative AI Engineer to develop and integrate large-scale generative models into physics simulation and scientific computing workflows.

You will operate at the intersection of foundation models, simulation systems, and applied scientific computing. This role is critical to building generative systems that meaningfully interact with numerical solvers, structured physics constraints, and real-world engineering pipelines.

You will work closely with research scientists, simulation engineers, and infrastructure teams to ensure generative models are robust, production-ready, and deeply integrated into core modeling workflows.

What You’ll Do

Model Development

• Develop, fine-tune, and adapt large-scale generative AI models
• Work on transformer-based architectures and emerging foundation model paradigms
• Implement conditioning mechanisms that incorporate physical constraints and simulation outputs
• Improve model generalization across diverse physics domains

Simulation & Systems Integration

• Integrate generative pipelines with simulation APIs and engineering systems
• Design workflows where generative models interact with numerical solvers and inverse design loops
• Build tooli...