AmazonSeattle 98109
Full Time

We are looking for Applied Scientists who are passionate about transforming industries through AI. This is a unique opportunity to not only listen to industry customers but also to develop AI and generative AI expertise in multiple core industries.

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Applied Scientist Iii, Industry Products

Amazon
Full Time Amazon, 400 9th Ave N, Seattle, WA 98109, United States
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Overview

AWS Industry Products (AIP) is a new AWS engineering organization chartered to build new AWS products by applying Amazon’s innovation mechanisms along with AWS digital technologies to transform the world, industry by industry. We dive deep with leaders and innovators to solve the problems which block their industries, enabling them to capitalize on new digital business models. Simply put, our goal is to use the skill and scale of AWS to make the benefits of a connected world achievable for all businesses. #BDSI_AIP

We are looking for Applied Scientists who are passionate about transforming industries through AI. This is a unique opportunity to not only listen to industry customers but also to develop AI and generative AI expertise in multiple core industries. You will join a team of scientists, product managers and software engineers that builds AI solutions in automotive, manufacturing, healthcare, sustainability/clean energy, and supply chain/operations verticals. Leveraging and advancing generative AI technology will be a big part of your charter as we seek to apply the latest advancements in GenAI to industry-specific problems

Using your in-depth expertise in machine learning and generative AI and software engineering, you will take the lead on tactical and strategic initiatives to deliver reusable science components and services that differentiate our industry products and solve customer problems. You will be the voice of scientific rigor, delivery, and innovation as you work with our segment teams on AI-driven product differentiators. You will conduct and advance research in AI and generative AI within and outside Amazon.

Extensive knowledge of both state-of-the-art and emerging AI methods and technologies is expected. Hands-on knowledge of generative AI, foundation models (including for vision and numerical data) and commitment to learn and grow in this field are expected. Prior research or industry experience in Automotive, Healthcare, Manufacturing, Supply Chain, or Sustainability would be a plus.

Inclusive Team Culture

Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences.

Work/Life Balance

Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.

Mentorship & Career Growth

Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.

We are open to hiring candidates to work out of one of the following locations:

Seattle, USA | Seattle, WA, USA

- 3+ years of building machine learning models for business application experience

- PhD, or Master's degree and 6+ years of applied research experience

- Knowledge of programming languages such as C/C++, Python, Java or Perl

- Experience with neural deep learning methods and machine learning

- 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.

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