Role overview

UniversalAGI is developing foundation models that learn physics directly from data to transform engineering simulation—a field where legacy numerical methods still dominate and engineers wait hours for answers. As an ML Engineer, you will own the execution layer that brings these models from research to production, working across data pipelines, training workflows, and evaluation systems. The team of 25 researchers and engineers is backed by prominent investors and advisors including Eric Schmidt and Turing Award winner David Patterson.

Key responsibilities

Build and maintain data preprocessing and data generation pipelines that support model training and evaluation efforts.
Execute training and fine-tuning workflows end-to-end while rapidly iterating on performance improvements.
Design and run benchmarking and evaluation suites to measure progress and measure impact on customer outcomes.
Work directly with PhD researchers to convert model architectures into production-grade, repeatable workflows.
Communicate results clearly through metrics, dashboards, and documentation while maintaining reproducible and high-quality work standards.

Requirements

Strong software engineering fundamentals including clean code practices, debugging, reliability, and reproducibility.
Solid grounding in ML foundations and hands-on experience across the full ML lifecycle from data through training, fine-tuning, and evaluation.
Demonstrated experience training or fine-tuning models in any modality—LLMs, computer vision, physics models, surrogate models, or similar work.
Commitment to high personal standards and measurable improvement on assigned metrics.
Resourcefulness in deciding when to implement quick solutions versus investing in robust infrastructure, with ability to justify tradeoffs by impact.
Comfort taking ownership of work end-to-end and being accountable for concrete outcomes.

What makes this role worth considering

This is an opportunity to work on cutting-edge problems at the intersection of deep learning and physics simulation, directly shaping the future of AI-powered engineering. You will collaborate closely with the founding team and world-class investors in the AI space, with genuine influence over company strategy. The role offers significant equity alongside competitive compensation, and the in-person culture in San Francisco—5 days per week—supports deep technical collaboration with a team that prioritizes face-to-face work.