The FAIM (Full Funnel Agentic Intelligence and Models) builds the agentic AI systems behind next-generation advertising. The team develops multimodal foundation models and LLM-driven agents that power shopper understanding, audience-aware creative generation, and agentic advertising workflows.
As a Principal Applied Scientist for the FAIM team, you will invent the models that help advertisers achieve desired outcomes. Today's state of the art models can setup ad campaigns to a specification, but do not understand advertising strategy well enough to help with abstract goals that real advertisers ask about like "how can I make more money."
This is a rare charter to build foundational optimization science where little exists today, spanning campaign recommendation, cross-product budget allocation, incrementality measurement, and long-term-sales modeling. You will set technical direction for a growing team, partner with engineering and product to take models from research to production at Amazon scale, and directly move advertiser ROAS and new-to-brand growth.
In this role you will
- Own the science vision for training models in our RL Gym from end-to-end
- Invent models for joint budget allocation, incrementality, and long-term value
- Take innovations from prototype to production serving live advertiser campaigns
- Raise the technical bar across scientists and engineers
Key job responsibilities
- Define the long-term scientific vision for our RL Gym based training process focusing on campaign optimization and other advertising workflows
- Translating ambiguous advertiser needs and competing objectives into a concrete science roadmap.
- Invent, prototype, and productionize machine learning and optimization solutions for joint budget allocation across sponsored ad products, spanning the shopper journey from awareness to purchase.
- Develop rigorous approaches to incrementality measurement and long-term-sales modeling that ground optimization in true advertiser value.
- Design and lead large-scale experiments and analyses to validate hypotheses and guide product direction.
- Partner closely with engineering and product to define technical contracts, data schemas, and serving systems that carry models into production.
- Raise the technical bar across science and engineering through mentorship, design reviews, and hands-on collaboration.
- Grow scientific talent and publish impactful research internally and at top-tier venues.
