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We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. 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 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 BalanceOur team puts a high value on work-life balance. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally.

We are committed to furthering our culture of inclusion. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere at AWS, we embrace our differences. Your work will directly impact millions of our customers in the form of products and services, as well as contributing to the wider research community. Job summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and Machine Learning (ML).As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. These rules save time and money by alerting the developer and terminating a problematic training job early. Debugger helps users define a set of conditions, in the form of built-in or custom rules, that are applied to this data, thereby enabling users to catch training issues as well as monitor and debug ML model training in real-time.

#TRAINING INSIGHTS OFFLINE#

Debugger is a new feature of Amazon SageMaker that automatically captures relevant data during training and evaluation and presents it for online and offline inspection. We present Amazon SageMaker Debugger, a machine learning feature that automatically identifies and stops underperforming training jobs.

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Just as unit tests boost traditional software development, an automated ML debugging library can save time and money. As state-of-the-art models grow in size and complexity, debugging becomes increasingly difficult. Identifying underperforming training jobs requires constant developer attention and deep domain expertise.

#TRAINING INSIGHTS MANUAL#

Manual debugging is a common productivity drain in the machine learning (ML) lifecycle.

Training insights