Applied Scientist, Demand Tech, Amazon Ads

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. Were looking for an Applied Scientist to help make sure the ads shoppers see are the right ones for them. Youll work across the science of how we rank value and bid on ads for Amazon DSP (Amazons 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.

Its 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 whats 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 youll 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; youll 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 Amazons 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 were 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 weeks experiment results then use an AI coding agent to get your next prototype built and ready to test 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. Youll move between hands-on science writing and shipping real production code and making the calls on your own work.

About the team
Were a group of scientists and engineers based in Edinburgh and London working to make Amazons 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. Youll 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.

- 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 Masters degree and experience in engineering technology computer science machine learning robotics operations research statistics mathematics or equivalent quantitative field

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

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