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Agentio builds AI-native infrastructure for creator advertising. The platform uses AI to turn creative and performance signals into campaign strategies, match brands with creators, recommend sponsorship prices, and review videos against brand requirements. The company learns from campaign feedback—brand responses to recommendations, creator acceptance rates, and content performance—to build self-improving loops.
As a Research Scientist, Applied AI, you will own research problems from formulation through experimentation, validation, and product application. Your work sits between research and product: you'll formulate research problems from product needs, test and extend methods from academia and industry, develop new approaches when existing methods fall short, and collaborate with engineering and product to translate successful research into production systems.
Key focus areas include:
**Multimodal Intelligence**: Build systems that understand creators, brands, briefs, and content across video, audio, image, and text.
**Ranking, Recommendation, and Prediction**: Develop models that predict creator-brand fit and campaign outcomes, making better decisions from sparse, heterogeneous feedback.
**Foundation Models and Agents**: Apply LLMs and multimodal models to campaign planning, discovery, creative understanding, execution, and optimization. Leverage Agentio's proprietary data for prompting, retrieval, fine-tuning, post-training, and specialized model training where appropriate.
**Learning and Decision-Making**: Turn campaign data into training signals, evals, and learning loops while tackling problems in optimization, exploration, measurement, and marketplace dynamics.
You will design rigorous experiments and evaluations, develop new methods where existing techniques are insufficient, and establish evidence that research advances improve product and marketplace outcomes. You'll help define Agentio's research agenda, identify high-value research questions, and set a high bar for experimental rigor across applied ML work. Part of the role involves deciding which advances in AI and ML are useful for Agentio and where new methods are needed.
**Requirements**:
- PhD in machine learning, AI, computer science, statistics, or a related field, with a track record of original ML research demonstrated through peer-reviewed publications or comparable research contributions.
- Depth in one or more areas such as foundation models, multimodal learning, recommendation and ranking, representation learning, reinforcement learning and decision-making, causal inference, optimization, or adjacent fields.
- Experience applying research to real products or production systems, including turning ambiguous product problems into research questions and translating successful results into measurable product improvements.
- Strong implementation skills: ability to prototype your own ideas, work directly with large datasets and modern ML stacks, and collaborate closely with engineers on productionization.
- Strong knowledge of current AI research and the judgment to distinguish technically interesting work from approaches that will matter in practice.
