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Agentio builds AI-native infrastructure for creator advertising, using AI to match brands with creators, recommend sponsorship prices, and optimize campaign strategies. The platform learns from campaign feedback—brand responses, creator acceptance rates, and content performance—to continuously improve predictions and decisions.
As a Research Engineer, Applied AI, you will advance this work through applied AI and ML research, developing models that improve platform predictions and automate creator advertising decisions. The role spans research and production: you'll evaluate academic and industry methods, adapt and train models on Agentio's proprietary data, develop novel approaches when existing methods fall short, and collaborate with engineering and product to ship solutions.
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 predicting creator-brand fit and campaign outcomes; make 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.
• Learning and decision-making: Convert campaign data into training signals and learning loops; tackle optimization, exploration, measurement, and marketplace dynamics.
You will own research problems end-to-end: from problem definition and experimentation through production. You'll evaluate AI/ML methods rigorously against Agentio's data, build experiments and production evaluations, develop novel models or algorithms when needed, help set the research agenda, and establish technical standards for applied ML.
QUALIFICATIONS:
• Demonstrated experience conducting applied ML research, evidenced through production ML systems, publications, open-source research, or comparable contributions. PhD or advanced degree in ML, AI, computer science, statistics, or related field is useful but not required.
• Depth in one or more areas: foundation models, multimodal learning, recommendation and ranking, representation learning, reinforcement learning and decision-making, causal inference, optimization, or adjacent fields.
• Ability to translate ambiguous product problems into tractable research questions, run rigorous experiments, and translate successful approaches into production systems.
• Strong engineering skills and experience taking ML work beyond prototypes.
• Strong knowledge of current AI research and judgment to determine which methods are useful in practice.
