RoleYou'll invent new ways to detect and scale the data that makes frontier AI models smarter. As frontier models continue to improve, model capability is increasingly driven by the quality of training data. You'll develop scalable systems that generate, curate, and improve high-quality supervision across coding, multimodal, and agentic tasks.
In this role, you also have an opportunity to be forward-deployed should it interest you, and work directly with the researchers at frontier labs to creatively scale new model capability strategies for improvement.
What You'll Own- Train and improve preference, reward, and ranking models from millions of human interactions
- Develop infrastructure for large-scale experimentation, model training, and specialize in online evaluation techniques
- Design systems that transform human preference data into reliable signals for downstream model evaluation and training
What We're Looking For- Data-pilled. Understand that data quality is the real bottleneck to superintelligence.
- Strong STEM background. You studied Computer Science, Machine Learning, Data Science, Statistics, Math, Engineering, Physics, or a related field.
- Experience building ML systems. You've trained preference models, built data pipelines, or worked on ML infrastructure that runs in production.
- Interested in how AI learns from human feedback. You're excited by solving problems at the intersection of human-model interaction.
Details- Location: San Francisco, Levi's Plaza. We sponsor visas and handle relocation.
- Work Schedule: Sunday-Friday. Saturdays are yours!
- Compensation: Competitive salary + meaningful equity. You'd be joining at the stage when ownership matters most.
