ML Engineer

About the role

📍 San Francisco | 🏢 5 Days Onsite

Location: Onsite in San Francisco

Compensation: Competitive Salary + Equity

Who We Are

Engineering simulation is one of the last major categories of software that AI hasn't rebuilt. The tools used to design aircraft, ships, reservoirs, and medical devices still run on numerical methods that are decades old, and an engineer can wait a full day for a single answer. UniversalAGI is building foundation models that learn physics directly from data, and they are already running in early deployments on real computational fluid dynamics and reservoir engineering problems for some of the largest industrial and defense organizations in the world.

We are a team of 25 researchers and engineers in San Francisco backed by Elad Gil (#1 Solo VC), Eric Schmidt (former Google CEO), 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).

About the Role

UniversalAGI is hiring an ML Engineer to help ship ML outcomes by owning the execution layer: data preprocessing/generation, training/fine-tuning, benchmarking, and delivering results.

What You’ll Do

- Build and maintain data preprocessing and data generation pipelines to support model training and evaluation.

- Run training and fine-tuning workflows end-to-end and iterate quickly on performance improvements.

- Design and execute benchmarking/evaluation suites to measure progress and customer outcomes.

- Collaborate with PhD expert researchers to operationalize model architectures into repeatable, production-grade workflows.

- Communicate results clearly (metrics, dashboards, short writeups) and maintain high-quality, reproducible work.

Qualifications

- Strong software engineering skills (clean code, debugging, reliability, reproducibility).

- Solid ML foundations and hands-on experience with the ML lifecycle: data → training/fine-tuning → evaluation/benchmarking. - Prior experience training or fine-tuning models (any modality/type - LLMs, computer vision, physics, surrogate models, etc.)

- Olympic athlete mindset: You have high standards for yourself and are obsessed with measurable improvement on the metrics you are delivering.

- Resourcefulness: you know when to do the “quick & correct” fix vs. when to invest in a robust solution, and you can justify the tradeoff with impact/

- Ownership: Comfortable owning work end-to-end and being accountable for measurable outcomes.

Bonus Qualifications

- Experience building data pre-processing pipelines for training ML models.

- Experience with benchmarking methodology, experiment design, and metric selection.

- Familiarity with distributed training / scalable compute workflows.