📍 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), 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).
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.
Experience in an FDE-style / delivery execution role (or similar “ship results fast” environments).
Cultural Fit
Technical Respect: Ability to earn respect through hands-on technical contribution
Intensity: Thrives in our unusually intense culture - willing to grind when needed
Customer Obsession: Passionate about solving real customer problems, not just publishing papers
Deep Work: Values long, uninterrupted periods of focused work over meetings
High Availability: Ready to be deeply involved whenever critical issues arise
Communication: Can translate complex model decisions to customers and team
Growth Mindset: Embraces the compounding returns of intelligence and continuous learning
Startup Mindset: Comfortable with ambiguity, rapid change, and wearing multiple hats
Work Ethic: Willing to put in the extra hours when needed to hit critical milestones
Team Player: Collaborative approach with low ego and high accountability
Bias for Action: Ships experiments fast, learns from failures, and iterates quickly
What We Offer
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
Benefits
We provide great benefits, including:
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.
How We’re Different
“The credit belongs to the man who is actually in the arena, whose face is marred by dust and
sweat and blood; who strives valiantly; who errs, who comes short again and again... who at the
best knows in the end the triumph of high achievement, and who at the worst, if he fails, at least
fails while daring greatly." - Teddy Roosevelt
At our core, we believe in being “in the arena. ” We are builders, problem solvers, and risk-takers who show up every day ready to put in the work: to sweat, to struggle, and to push past our limits. We know that real progress comes with missteps, iteration, and resilience. We embrace that journey fully knowing that daring greatly is the only way to create something truly meaningful.
If you're ready to train the models that will revolutionize physics simulation, push the boundaries of what AI can learn, and deliver real impact, UniversalAGI is the place for you.
Skills Required
- Strong software engineering skills, including clean code, debugging, reliability, and reproducibility
- Solid machine learning foundations
- Hands-on experience across the machine learning lifecycle, including data, training or fine-tuning, and evaluation or benchmarking
- Prior experience training or fine-tuning machine learning models
- High standards and focus on measurable improvement
- Resourcefulness in balancing quick fixes with robust solutions
- Ability to own work end-to-end and be accountable for measurable outcomes
- Experience building data preprocessing pipelines for machine learning model training
- Experience with benchmarking methodology, experiment design, and metric selection
- Familiarity with distributed training or scalable compute workflows
- Experience in an FDE-style delivery execution role or similar environment
What We Do
UniversalAGI is automating physical systems engineering across the entire product lifecycle with artificial intelligence.
