Senior Applied Scientist, Ads Search and Relevance

2 days ago


Santa Clara CA United States Amazon Full time
Senior Applied Scientist, Ads Search and Relevance

Job ID: 2799470 | Amazon.com Services LLC - A57

When customers search for products on the Amazon website, they often see brand advertisements displayed right below the search bar. These ads are part of the Sponsored Brands (SB) program. Our team, the SB Search and Relevance team, works on solving challenges to retrieve the most relevant ads for a customer's search query. A customer's search query is typically a short, free-form text consisting of just a few words. Our algorithm needs to understand the customer's underlying intention from this limited information. At the same time, each advertisement consists of various elements like text descriptions, images, videos, and more. Our algorithm also needs to comprehend the content of these ads and identify the most relevant one from the large pool of ad candidates.

As Amazon's advertising business is growing rapidly, we are looking for experienced applied scientists. As an Applied Scientist on this team, you will:

  1. Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity.
  2. Apply deep learning and natural language processing to improve information retrieval and relevance.
  3. Design and run A/B experiments. Evaluate the impact of your optimizations and communicate your results to various business stakeholders.
  4. Optimize deep learning inference latency by utilizing methods like knowledge distillation.
  5. Work with software development engineers and write code to bring models into production.
  6. Recruit Applied Scientists to the team and provide mentorship.

Impact and Career Growth

You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon Define a long-term science vision for our advertising business, driven fundamentally from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding.

BASIC QUALIFICATIONS
  1. 3+ years of building machine learning models for business application experience
  2. PhD, or Master's degree and 6+ years of applied research experience
  3. Experience programming in Java, C++, Python or related language
  4. Experience with neural deep learning methods and machine learning
PREFERRED QUALIFICATIONS
  1. Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
  2. Experience with large scale distributed systems such as Hadoop, Spark etc.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit This position will remain posted until filled. Applicants should apply via our internal or external career site.

Posted: July 26, 2024 | Updated: October 10, 2024

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