UniversalAGI
ML Engineer
San Francisco · Exec
Sponsorship not specifiedDetected 146 days ago
DatabricksMachine LearningDeep LearningComputer VisionLLMsANSYSCommunicationCollaboration
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.
Responsibilities
- Build and maintain data preprocessing and data generation pipelines to support model training and evaluation.
- 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.
- UniversalAGI is building OpenAI for Physics.
- We're building foundation AI models for physics that enable end-to-end industrial automation from initial design through optimization, validation, and production.
- We're building a high-velocity team of relentless researchers and engineers that will define the next generation of AI for industrial engineering.
- Team Building & Fun Activities.
- Immigration support.
Requirements
- Solid ML foundations and hands-on experience with the ML lifecycle: data → training/fine-tuning → evaluation/benchmarking.
- 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/
- You have high standards for yourself and are obsessed with measurable improvement on the metrics you are delivering.
- Ability to earn respect through hands-on technical contribution
- Strong software engineering skills (clean code, debugging, reliability, reproducibility).
- Prior experience training or fine-tuning models (any modality/type - LLMs, computer vision, physics, surrogate models, etc.)
- 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.
- Cultural Fit
Nice to have
- 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).
- 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
Compensation
- Competitive Salary + Equity
Benefits
- Competitive compensation and equity.
- Competitive health, dental, vision benefits paid by the company.
- Flexible vacation.
- AI tools stipend.
- Monthly commute stipend.
- Monthly wellness / fitness stipend.
Company info
- AI startup based in San Francisco and 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).
- If you're passionate about AI, physics, or the future of industrial innovation, we want to hear from you.
- Can translate complex model decisions to customers and team
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This listing is sourced directly from UniversalAGI's careers page and normalized into a canonical job model.