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📍 San Francisco | Work Directly with CEO & founding team | Report to CEO | OpenAI for Physics | 🏢 5 Days Onsite MACHINE LEARNING ENGINEER Location: Onsite in San Francisco Compensation: Competitive Salary + Equity Who We Are UniversalAGI is building OpenAI for Physics. 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). 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. If you're passionate about AI, physics, or the future of industrial innovation, we want to hear from you. 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 physi