Senior Applied Scientist, Amazon Ads, Demand Tech , Amazon Advertising, Demand Tech

Palo Alto, California, United States💼 Full-time💰 $167,100–$167,100🗓 2026-06-24 → 2026-07-31

Core

Design and improve deep learning models for multi-task prediction (click, conversion, page view, incrementality) to optimize programmatic advertising performance at massive scale.

Role type

Senior Applied Scientist (Machine Learning)

Builds

Response prediction and incrementality models powering bid optimization for Amazon DSP and Sponsored Display

Domain

Programmatic advertising, machine learning, deep learning

Deliverable

production ML models

Required skills

multi-task deep learning, neural network architectures, online A/B experimentation, statistical analysis, model calibration, real-time inference optimization, distributed systems programming

Preferred skills

large scale distributed systems, modeling tools (TensorFlow, PyTorch, scikit-learn), signal processing

Technologies

SageMaker, GPU inference, real-time feature stores, OpenRTB, Java, C++, Python, Spark, Hadoop

Responsibilities

Design and improve deep learning models for multi-task prediction, build and iterate on calibration mechanisms, integrate novel signals into production models, run online A/B experiments, collaborate on model serving infrastructure, mentor scientists

Seniority

Senior, hands-on IC with research leadership

Rewrite

## Responsibilities - 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 ## Requirements - 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 ## Nice to Have - 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. ## Benefits - Base salary range: $192,200.00 - $260,000.00 USD annually (USA, CA, Palo Alto) - Base salary range: $167,100.00 - $226,100.00 USD annually (USA, WA, SEATTLE) - Comprehensive benefits including health insurance, 401(k) matching, paid time off, and parental leave - 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 - Inclusive culture and support for workplace accommodations - Equal opportunity employer with no discrimination based on protected veteran status, disability, or other legally protected status - Los Angeles County applicants: Consideration for qualified applicants with arrest and conviction records

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