Senior Applied Scientist, Amazon Ads, Demand Tech , Amazon Advertising, Demand Tech
What you will do
Own end-to-end response prediction — design and improve deep learning models for multi-task prediction (click, conversion, page view, incrementality) serving at inference latencies under 10ms at millions of TPS
Build and iterate on calibration mechanisms that keep prediction accuracy stable across rapidly shifting supply distributions
Integrate novel signals (OpenRTB features, customer behavioral sequences, supply quality feeds) into production models to improve optimization quality
Run online A/B experiments at scale, analyze results with statistical rigor, and translate offline gains into measurable business impact
Collaborate closely with engineers on model serving infrastructure (SageMaker, GPU inference, real-time feature stores) to deploy models efficiently at scale
Mentor scientists on the team and contribute to the broader Amazon ML science community through papers, conferences, and internal deep dives
What makes this role unique
Direct business impact: Your models determine bid prices for billions of daily ad impressions — a 1% prediction improvement translates to tens of millions in advertiser value
Technical depth at scale: Multi-task deep learning architectures serving real-time inference across multiple global regions under strict latency constraints
Diverse problem space: From signal-sparse open internet prediction to calibration under distribution shift, from incrementality measurement to cost-efficient GPU inference
Autonomy and ownership: End-to-end ownership from problem framing through research, experimentation, production deployment, and business metric monitoring
Impact and career growth
Amazon is investing heavily in building a world-class advertising business. Your work directly influences how Amazon's advertising products optimize campaign performance for advertisers worldwide. You will work with a highly motivated, collaborative team with a broad mandate to experiment and innovate. You will have opportunities to present to senior leadership, define long-term science vision, attend external conferences (NeurIPS, KDD, ICML), and shape the direction of ML-driven advertising at Amazon.