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

Overview

In Amazon Advertising, we apply Machine Learning at massive scale to optimize programmatic advertising performance. The Demand Tech team owns response prediction and incrementality models that power bid optimization across Amazon DSP and Sponsored Display — determining how billions of ad impressions are valued and served daily across Amazon‑owned properties, the open internet, and third‑party exchanges.

We are looking for a talented Senior Applied Scientist to join our team of scientists and engineers working on high‑impact prediction systems that directly drive advertiser KPIs (CPA, ROAS, incrementality) across endemic and non‑endemic programmatic advertising.

• 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 10 ms 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 (Sage Maker, 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.

• 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.

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.

• 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.

• 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.

The base salary range for this position is listed below. Your Amazon package will include sign‑on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location.

USA, CA, Palo Alto –  –  USD annually
USA, WA, SEATTLE –  –  USD annually

Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Job duties for this position include: work safely and cooperatively with…