Sr. Applied Scientist, Amazon Ads

Amazon Amazon · Big Tech · Seattle, WA · Applied Science

Senior Applied Scientist at Amazon Ads focusing on applying cutting-edge generative AI and LLMs to the advertising life cycle. The role involves researching, developing, and deploying ML solutions for ranking, personalization, NLP, computer vision, recommender systems, and LLMs. It requires driving end-to-end projects, building and optimizing models, running A/B experiments, and developing scalable ML processes. The role emphasizes impacting millions of customers and advertisers through innovative ML solutions at massive scale.

What you'd actually do

  1. Research and implement cutting-edge ML approaches, including applications of generative AI and large language models
  2. Develop and deploy innovative ML solutions spanning multiple disciplines – from ranking and personalization to natural language processing, computer vision, recommender systems, and large language models
  3. Drive end-to-end projects that tackle ambiguous problems at massive scale, often working with petabytes of data
  4. Build and optimize models that balance multiple stakeholder needs - helping customers discover relevant products while enabling advertisers to achieve their goals efficiently
  5. Build ML models, perform proof-of-concept, experiment, optimize, and deploy your models into production, working closely with cross-functional teams including engineers, product managers, and other scientists

Skills

Required

  • 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

Nice to have

  • 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.

What the JD emphasized

  • building machine learning models for business application experience
  • applied research experience
  • neural deep learning methods and machine learning

Other signals

  • generative AI
  • large language models
  • ranking algorithms
  • real-time bidding
  • creative optimization
  • measurement solutions
  • petabytes of data
  • A/B experiments