Machine Learning Scientist (f/m)
The Core Machine Learning team is comprised of technical leaders who develop large-scale platforms for machine learning, assist the benchmarking and future development of existing machine learning applications across Amazon, and help develop novel applications that optimize Amazon's systems using cutting edge quantitative techniques. The ML team innovates algorithms to drive automated decisions at scale in all corners of the company.
In this position, you will work to invent and build systems which streamline Data Science process and automate data-driven decisions in all corners of the company. You will be close to all stages of the development process, from business tasks, to system development and algorithm research.
Being part of the Machine Learning team at Amazon is one of the most exciting job opportunities in the world today. If you are deeply technical, self-motivated, know how to deliver, innovative, and strive to build solutions to challenging problems that directly affect millions of people: there may be no better place than Amazon for you to impact the world!
We are looking for scientific leaders with a passion for Machine Learning applications and experience in distributed algorithms, probabilistic Machine Learning, and deep learning. Applicants should have an outstanding academic track-record and publications in top-tier conferences and journals. Experience with scalable storage systems, distributed computation, and Machine Learning frameworks such as MXNet, scikit-learn, and Spark ML is preferred. C++ or Java as well as scripting languages like Python and good knowledge of Linux development are required.
- MSc in Computer Science, Statistics or related field and +5 years recent Java, C++, or Python experience or PhD with relevant coding experience
- Publications in top-tier Machine Learning conferences and journals
- Intermediate Linux/UNIX skills
- Strong sense of ownership, urgency and drive
- Strong communication skills
- Strong focus on applied tasks
- PhD +2 years Post-doc or industry experience
- Experience with distributed data storage and data processing frameworks
- Experience with Machine Learning toolboxes and frameworks
- Experience in online learning, reinforcement learning and/or active learning
- Experience using notebook applications
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