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

Basic Qualifications

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning

Preferred Qualifications

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with large scale distributed systems such as Hadoop, Spark etc.