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Applied Scientist, Amazon Ads

Amazon.com Inc
  • San Francisco, CA
    3 days ago

    Job Description

    Amazon Ads runs one of the largest real-time decision systems in the world. To meet advertiser goals, we process >4 million ad requests per second, and this team decides which of them are worth the compute. We mine a petabyte-scale stream of traffic to tell in-demand, valuable requests apart from wasteful ones, filtering out the noise and forwarding only what brings value to advertisers, all within milli-seconds. Our solutions save millions in compute costs and drive long-term growth for Amazon"s Retail and Marketplace businesses.

    Why you"ll love this role: The scientific challenge here is scale and signal: large-scale ML on enormous, high-velocity data, optimization and auction dynamics, and causal measurement of what actually moves the business. Generative AI and LLMs are tools you reach for when they help, not the point of the work. The impact also reaches beyond Amazon: we donated the Dynamic Traffic Engine (DTE) to the IAB Tech Lab as an open-source framework that lets demand-side platforms signal which bid requests they value, so supply-side platforms send fewer wasteful ones and QPS waste drops across the whole programmatic supply chain. We are a highly motivated, collaborative team with an entrepreneurial spirit and a broad mandate to experiment and innovate. You will work alongside talented engineers, product managers, and scientists in a culture that rewards bias for action, innovation, and experimentation. What makes this role unique is the combination of scientific rigor with real-world impact at a scale very few systems reach.

    Your impact and career growth: Amazon Ads is investing heavily in AI and ML, creating room for scientists to innovate and make their mark. As an Applied Scientist, you will own the science for well-defined supply-optimization problems and take your work end to end, from framing the approach through experimentation and into production with measured business impact. You will partner closely with engineers to productionize your models and with product managers to tie your science to customer and advertiser outcomes. You will share your findings, begin to mentor teammates, and grow your influence on the team"s technical direction over time. This is your opportunity to do high-rigor applied science in one of the fastest-growing businesses at Amazon.

    Learn more about Amazon Ads: https://advertising.amazon.com/

    Key job responsibilities

    As an Applied Scientist in Amazon Ads, you will:

    • Conduct hands-on data analysis and build large-scale ML models and pipelines for supply optimization.
    • Take your models end to end, from proof of concept through experimentation, optimization, and deployment into production, working closely with software engineers to productionize them and with product managers and fellow scientists along the way.
    • Design and run A/B experiments and causal analysis to validate hypotheses, draw insights from petabyte-scale data, and measure real business impact.
    • Research and apply advanced ML, including generative AI and large language models, to demand prediction and traffic filtering.
    • Drive end-to-end optimization projects that tackle ambiguous problems at massive scale, developing scalable, efficient processes for model development, validation, and deployment that optimize traffic monetization while maintaining customer experience.

    Numbers & Facts

    LocationSan Francisco, CA
    IndustryRetail
    Company Size10,000 employees or more
    Year Founded1994
    Websitehttp://Amazon.com/militaryroles

    About Company

    At Amazon, we don’t wait for the next big idea to present itself. We envision the shape of impossible things and then we boldly make them reality. So far, this mindset has helped us achieve some incredible things. Let’s build new systems, challenge the status quo, and design the world we want to live in. We believe the work you do here will be the best work of your life.

    Wherever you are in your career exploration, Amazon likely has an opportunity for you. Our research scientists and engineers shape the future of natural language understanding with Alexa. Fulfillment center associates around the globe send customer orders from our warehouses to doorsteps. Product managers set feature requirements, strategy, and marketing messages for brand new customer experiences. And as we grow, we’ll add jobs that haven’t been invented yet.

    It’s Always Day 1
    At Amazon, it’s always “Day 1.” Now, what does this mean and why does it matter? It means that our approach remains the same as it was on Amazon’s very first day – to make smart, fast decisions, stay nimble, invent, and stay focused on delighting our customers. In our 2016 shareholder letter, Amazon CEO Jeff Bezos shared his thoughts on how to keep up a Day 1 company mindset. “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight,” he wrote. “A customer-obsessed culture best creates the conditions where all of that can happen.” You can read the full letter here

    Our Leadership Principles
    Our Leadership Principles help us keep a Day 1 mentality. They aren’t just a pretty inspirational wall hanging. Amazonians use them, every day, whether they’re discussing ideas for new projects, deciding on the best solution for a customer’s problem, or interviewing candidates. To read through our Leadership Principles from Customer Obsession to Bias for Action, visit https://www.amazon.jobs/principles

    Skills

    • Analysis Skillsunmatched
    • Artificial Intelligence (AI)unmatched
    • Artificial Intelligence (AI) Programming Languagesunmatched
    • Business Growthunmatched
    • Customer Experienceunmatched
    • Data Analysisunmatched
    • Entrepreneurshipunmatched
    • Laboratoryunmatched
    • Mentoringunmatched
    • Model Validationunmatched
    • Modeling Languagesunmatched
    • Open Sourceunmatched
    • Process Modelingunmatched
    • Proof of Conceptunmatched
    • Retailunmatched
    • Scalable System Developmentunmatched
    • Software Engineeringunmatched
    • Supplier Optimizationunmatched
    • Supply Chainunmatched
    • Team Playerunmatched

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