ML Platform Engineer

📍 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 a ML Platform Engineer to build and own the execution platform powering our research and customer deployments: data generation + simulation orchestration + training/fine-tuning infrastructure + benchmarking pipelines + production deployments in customer environments.

You’ll work closely with the CEO and founding team to turn research into repeatable, scalable, reliable systems - internally and in customer infrastructure. This is a “ship outcomes” role: your work directly determines how fast we can iterate, how reproducible our results are, and how reliably we deliver in production.

What You’ll Do

Build the foundation platform (internal):

• Build and operate scalable infrastructure for data generation and simulation workflows (job orchestration, scheduling, queues, retries, observability).

• Build reproducible pipelines for training/fine-tuning and benchmarking (artifact/version management, experiment tracking, dataset lineage).

• Own cost/performance tradeoffs across compute, storage, networking, and runtime efficiency.

Deploy to customers (external):

• Lead deployments of our stack into customer cloud/on-prem environments (AWS/GCP/Azure + hybrid), including secure networking, permissions, and data movement.

• Build robust deployment patterns: environment provisioning, CI/CD, rollbacks, monitoring, and incident response.

• Partner with customers to ensure reliability and repeatability under real-world constraints (security, compliance, infra limits, data governance).