Applied Scientist, Demand Tech, Amazon Ads (Advertising)

Amazon; Greater London; On-site; GBP 110,000 - 150,000; Full time

Job summary

Amazon Advertising in London seeks an Applied Scientist to advance ML models that rank, value, and bid on ads across Amazon DSP, ensuring ads are relevant to shoppers in real time. You will work across ranking, deep learning, and information retrieval, owning problems end to end from framing to production.

Join a team of scientists and engineers based in London and Edinburgh, with opportunities to publish and present at top conferences.

Qualifications

• Patents or publications in top-tier conferences or journals.
• Programming in Java, C++, Python or related language.
• Experience in algorithms, data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing.
• PhD or a Master's degree and experience in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field.

Responsibilities

• Design and improve the models that decide how ads are ranked, valued, and priced — including how relevant an ad is to the page and the shopper.
• Apply and extend state-of-the-art techniques across e.g. ranking, deep learning, and information retrieval.
• Own problems end to end: frame them, prototype, experiment, and ship them to production.
• Balance competing objectives — shopper experience, advertiser and publisher value, and Amazon's business — into models that hold up across placements and marketplaces.
• Communicate your work clearly to both business and science audiences, tailoring how you share it to each.
• Write and ship your own production code backed by strong engineering support — we're all builders here.
• Move fast with the best tools available, including modern AI coding assistants and agents.

Skills

Patents or publications

Java

C++

Python

Algorithms and data structures

High-performance computing

Education

PhD or Master's degree in engineering, technology, CS, ML

Job description

Applied Scientist, Demand Tech, Amazon Ads

Job ID: 10477594 | Amazon Development Centre (London) Limited

In Amazon Advertising, we apply machine learning at massive scale to optimize the prediction, ranking, and bidding behind every ad — deciding, in milliseconds, which ads to show shoppers and how to value them. We're looking for an Applied Scientist to help make sure the ads shoppers see are the right ones for them. You'll work across the science of how we rank, value, and bid on ads for Amazon DSP (Amazon's Demand-Side Platform) — including how we judge whether an ad is a good fit for the page a shopper is on and for the shopper themselves.

It's high-scale, low-latency, customer-facing science: your models run live in front of millions of shoppers under tight real-time constraints. The questions are genuinely open — how do you tell whether an ad is relevant to someone, how do you balance what's good for shoppers, advertisers, and Amazon, and how do you keep getting that right as shopping behavior and inventory shift underneath you? Your work will have real impact, and you'll have room to shape where we take it.

A few things make this stand out: your models touch a huge share of the ads shoppers see every day, so even small improvements add up fast; you'll run modern ML live under strict latency limits, across regions and very different types of ad inventory; and the problem space is rich — from how we value and bid on ads, to keeping models stable as traffic shifts, to what makes an ad a good fit for a shopper.

Key job responsibilities

• Design and improve the models that decide how ads are ranked, valued, and priced — including how relevant an ad is to the page and the shopper.
• Apply and extend state-of-the-art techniques across e.g. ranking, deep learning, and information retrieval.
• Own problems end to end: frame them, prototype, experiment, and ship them to production.
• Balance competing objectives — shopper experience, advertiser and publisher value, and Amazon's business — into models that hold up across placements and marketplaces.
• Communicate your work clearly to both business and science audiences, tailoring how you share it to each.
• Write and ship your own production code backed by strong engineering support — we're all builders here.
• Move fast with the best tools available, including modern AI coding assistants and agents.

A day in the life

You might start by digging into last week's experiment results, then use an AI coding agent to get your next prototype built and ready to test in production. In the afternoon you could be sketching a new way to measure ad relevance, reading a recent paper that bears on it, and talking it through with a senior scientist on the team. You'll move between hands-on science, writing and shipping real production code, and making the calls on your own work.

About the team

We're a group of scientists and engineers based in Edinburgh and London, working to make Amazon's ads more performant and relevant. We sit within a larger team spread primarily across New York City and the UK, and we have a broad mandate to build and experiment. You'll work alongside senior applied scientists you can learn from, with the data and infrastructure to do the work well and room to grow — with opportunities to attend top conferences (e.g., NeurIPS, KDD, ICML) and take on more scope over time.

Basic Qualifications

• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience programming in Java, C++, Python or related language
• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
• PhD or a Master's degree and experience in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field

Preferred Qualifications

• Experience in professional software development
• Experience applying theoretical models in an applied environment

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