Senior Applied Scientist, Books Content Experience
Are you interested in changing the Digital Reading Experience? We are from Kindle Books Team looking for a set of Scientists to take the reading experience in Kindle to the next level with a set of innovations!
We envision Kindle as the place where readers find the best manifestation of all written content optimized with features that enable them to get the most out of reading, and creators are able to realize their vision to customers quickly and at scale. Every time customers open their content, regardless of surface, they start or restart their reading in a familiar, useful, and engaging place. We achieve this by building a strong foundation of core experiences and act as a force multiplier and partner for content creators (directly or indirectly) to easily innovate on top of Kindle's purpose-built content experience stack in a simple and extensible way. Our goal is to foster long-lasting reading habits and make us the preferred destination for enriching literary experiences.
We are building an In The Book Science team and looking for Scientists who are passionate about Reading and are willing to take Reading to the next level. Every Book is a complex structure with different entities, layout, format, and semantics, with more than 17MM eBooks in our catalog. We are looking for experts in all domains like core NLP, Generative AI, CV, and Deep Learning Techniques for unlocking capabilities like analysis, enhancement, curation, moderation, translation, transformation, and generation in Books based on Content structure, features, Intent & Synthesis.
Key Job Responsibilities
1. 5+ years of building machine learning models for business application experience
2. PhD, or Master's degree and 6+ years of applied research experience
3. Knowledge of programming languages such as C/C++, Python, Java, or Perl
4. Experience programming in Java, C++, Python or related language
5. Expertise in one of the applied science disciplines, such as machine learning, natural language processing, computer vision, Deep learning
6. Ability to use reasonable assumptions, data, and customer requirements to solve problems
7. Initiate the design, development, execution, and implementation of smaller components with input and guidance from team members
8. Work with SDEs to deliver solutions into production to benefit customers or an area of the business
9. Assume responsibility for the code in your components; write secure, stable, testable, maintainable code with minimal defects
10. Understand basic data structures, algorithms, model evaluation techniques, performance, and optimality tradeoffs
11. Follow engineering and scientific method best practices; get designs, models, and code reviewed; test code and models thoroughly
12. Participate in team design, scoping, and prioritization discussions; map a business goal to a scientific problem and map business metrics to technical metrics
13. Invent, refine, and develop solutions to ensure they are meeting customer needs and team goals; keep current with research trends in your area of expertise and scrutinize results
14. Experience in mentoring junior scientists
A Day in the Life
You will be working with a group of talented scientists on researching algorithms and running experiments to test solutions to improve our experience. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, model development, and productionizing the same. You will mentor other scientists, review and guide their work, and help develop roadmaps for the team.
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.
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