ML Engineer

📋 Job Overview

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. The role is based in San Francisco with a 5 days a week on-site requirement.

🏢 About UniversalAGI

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), 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).

🎯 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.

✅ Key Responsibilities

• 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.

📌 Required 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.

⭐ Desirable Experience

• 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.
• Experience in an FDE-style / delivery execution role (or similar “ship results fast” environments).

🎁 Benefits

• Opportunity to define the future of physics AI from the ground up
• Work on cutting-edge problems at the intersection of deep learning and physics simulation
• 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
• 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

🛂 Visa & Eligibility

Immigration support is provided.