Amazon Senior Applied Scientist Vertical Search 

India, Karnataka 

770679804

Key job responsibilities
* Build machine learning models for Product Search.
* Develop new ranking features and techniques building upon the latest results from the academic research community.
* Propose and validate hypothesis to direct our business and product road map. Work with engineers to make low latency model predictions and scale the throughput of the system.
* Focus on identifying and solving customer problems with simple and elegant solutions.
* Design, develop, and implement production level code that serves billions of search requests. Own the full development cycle: design, development, impact assessment, A/B testing (including interpretation of results) and production deployment.
* Collaborate with other engineers and related teams within A9.com and Amazon.com to find technical solutions to complex design problems.
* Take ownership. Understand the needs of various search teams, distill those into coherent projects, and implement them with an eye on long-term impact.
* Be a leader. Use your expertise to set a high bar for the team, mentor team members, set the tone for how to take on and deliver on large impossible-sounding projects.
* Be ambitious. Find and eagerly tackle hard problems.
* Be curious. You will work alongside systems engineers, machine learning scientists, and data analysts. Your effectiveness and impact will depend on discussing problems with and learning from them. You will have access to the cutting-edge technologies and vast technical tools and resources of Amazon and will need to learn how to use them effectively.
* Be customer focused. Work backwards from customer problems, figure out elegant solutions, and implement them for speed and scalability.

- 3+ years of 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
- Peer reviewed scientific contributions in relevant field