📍 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 our first Technical Product Manager to build and own the execution of our core platform from 0 to 1. You will build the product that replaces slow, compute-heavy numerical solvers with our proprietary Latent Interacting Field Transformer (LIFT) architecture, taking raw geometries and delivering physics-consistent outputs at over 95% accuracy in seconds.

You’ll work closely with the CEO alongside an elite team of 90%+ PhDs and Physical AI pioneers to turn complex workflows from prime defense contractors, top-tier automotive OEMs, and national energy companies into intuitive, scalable product solutions. This is a “ship outcomes” role: your work directly determines how users interact with our models, how fast we can iterate on features, and how seamlessly we deliver value to the most demanding engineering teams in the world.

What You'll Do

Take our product from 0 to 1: Own the customer-facing experience across the UniversalAGI platform, including our web UI, API, CLI, and CAD environment integrations (like FreeCAD, CATIA, and SolidWorks).

Translate complex workflows: Deeply understand the simulation bottlenecks of world-class engineers across defense, aerospace, maritime, and energy, and turn those into precise product requirements.

Define the roadmap: Shape the rollout of our automated data generation pipelines, active learning capabilities, uncertainty quantification, and geometry morphing tools.

Bridge AI and Engineering: Act as the translation layer between our elite machine learning researchers, physics domain experts, and enterprise customers.

Support complex deployments: Design product features that accommodate strict enterprise deployment requirements, including ITAR-ready, on-premise, and air-gapped sovereign environments.

Qualifications

0-to-1 Product Experience: Strong product management skills with a proven track record of shipping highly technical products from scratch in an early-stage startup environment.

Deep Technical Fluency: Comfortable navigating APIs, cloud infrastructure, and holding your own in conversations with world-class PhD researchers and engineering leaders.

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

Customer Obsession: You possess the ability to extract qualitative feedback from sophisticated enterprise buyers and distill it into precise product requirements.

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 with physical engineering, computational fluid dynamics (CFD), finite element analysis (FEA), or reservoir simulation.

Experience with 3D applications, industrial design software, or automated meshing and post-processing tools.

Experience deploying into regulated / security-sensitive environments (gov/defense/enterprise).