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Intelligence is a product lab company building evaluation platforms and data engines for frontier AI models. The company operates DesignArena (5.2M+ users, referenced by Andrew Ng, Elon Musk, and Demis Hassabis), Prediction Arena, and Social Arena—platforms that rigorously evaluate state-of-the-art multimodal models across design, web development, game development, image, video, audio, and slide generation. The team is talent-dense with members from Harvard and Berkeley, backed by Tier 1 investors including Index Ventures, Y Combinator, SV Angel, and Figma Ventures.
As a Member of Technical Staff, ML Engineer, you will build the machine learning systems powering the evaluation platform and next-generation data engine. Your core responsibilities include training human preference models, building large-scale data pipelines, and developing infrastructure that transforms millions of human interactions into reliable signals for evaluating and improving frontier AI models. You'll specialize in online evaluation techniques and design systems that convert human preference data into actionable signals for downstream model evaluation and training.
The role offers the opportunity to be forward-deployed with frontier labs, working directly with researchers to creatively scale new model capability strategies. You'll own preference, reward, and ranking model development; infrastructure for large-scale experimentation and model training; and systems that bridge human feedback and AI improvement.
Required qualifications include a strong STEM background (Computer Science, Data Science, Statistics, Math, Engineering, Physics, or related field) and demonstrated experience building ML systems in production—such as training preference models, building data pipelines, or working on ML infrastructure. You should be excited by problems at the intersection of human-model interaction and RLHF-adjacent challenges.
The position is based at Levi's Plaza in San Francisco. The company sponsors visas and handles relocation. Work schedule is Sunday-Friday with Saturdays off. Compensation includes competitive salary and meaningful equity at an early stage where ownership matters significantly.
