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Intelligence is a product lab company building frontier AI evaluation platforms. The company operates DesignArena (5.5M+ users in 8 months), a benchmark for AI-generated visuals referenced by industry leaders including Andrew Ng, Elon Musk, and Demis Hassabis. The team is talent-dense with 11 members from Harvard and Berkeley, backed by Tier 1 VCs including Index Ventures, Y Combinator, and Paul Graham.
As a Member of Technical Staff for Frontier Evals, you will define how frontier AI models are measured and evaluated. You'll design novel benchmarks, run large-scale experiments, analyze model behavior across multimodal capabilities (design, web dev, game dev, image, video, audio, slide generation), and build evaluation methodologies that become trusted industry signals.
Key responsibilities include:
- Designing genuinely hard and useful evaluations that measure frontier model performance on real-world tasks
- Investigating model failures and identifying what they reveal about emerging capabilities
- Publishing research, technical reports, and analyses that shape frontier model evaluation
- Building evaluation tools and public leaderboards trusted by frontier labs like OpenAI
- Contributing to industry-leading benchmarks that influence how AI progress is measured
You'll work directly with frontier model providers, conducting rigorous evaluations before capabilities become public knowledge. Your work will be tracked by industry leaders and shape the evaluation standards across the AI industry.
Requirements: Strong STEM background (Computer Science, Data Science, Statistics, Math, Engineering, Physics, or related field). Deep curiosity about frontier AI models, understanding of model behavior, and genuine intellectual drive to work at the frontier of AI development. Willingness to roll up your sleeves and do hands-on technical work.
Location: San Francisco (Levi's Plaza). Visa sponsorship and relocation support provided. Work schedule: Sunday-Friday (Saturdays off). Compensation: Competitive salary plus meaningful equity at an early stage where ownership matters significantly.
