Applied Scientist I, Ads Trust Science

Applied Scientist I, Ads Trust Science, Bangalore

Every ad that reaches a customer through Amazon DSP has passed through a system you'll help build. The Ads Trust Science team protects one of the world's largest advertising ecosystems , We moderate millions of ad requests a day across 1P and 3P publisher platforms globally across different languages. And we're scaling by an order of magnitude from here.

This isn't a research team working in isolation, it's a science team whose models ship into production and directly decide what customers should not see. You'll build ML models that understand ads the way a human reviewer would: reading text, parsing images, catching intent, across languages and cultures. You'll work at the frontier of multimodal content understanding, combining vision and language models to solve problems that don't have off-the-shelf solutions, because the scale and stakes are unlike almost anywhere else.

What you'll actually do:

Design and train multimodal (vision + language) ML models that flag or clear ads at massive scale

Push these models beyond English. You will build systems that generalize across languages, scripts, and cultural context

Take models from notebook to production: write the code, build the pipelines, and own the systems that moderate millions of ads a day

Partner closely with engineers and fellow scientists to turn a research idea into something that runs reliably at Amazon scale

See your work matter immediately: a model you ship this quarter is protecting customers next quarter

Why this role:

Rare combination: real research problems (open-vocabulary understanding, low-resource languages, cross-modal reasoning) with real production impact and real scale

You won't be the only scientist working on a narrow slice rather you'll have end-to-end ownership from problem formulation to deployment

Trust and Safety at Amazon's size is a genuinely hard, underexplored ML problem. Most of what you'll build doesn't exist in a textbook yet

Basic Qualifications

Experience building machine learning models or developing algorithms for business application

Experience in state-of-the-art deep learning models architecture design and deep learning training and optimization and model pruning

Currently has, or is in the process of obtaining, a Master's degree or equivalent in Engineering, Computer Science, Machine Learning, Operations Research, Statistics, or related fields

Experience researching about machine learning, deep learning, NLP, computer vision, data science

Experience programming in Java, C++, Python or related language

Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse

Preferred Qualifications

Experience applying theoretical models in an applied environment

Have publications at top-tier peer-reviewed conferences or journals

Master's degree

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