Offers “Amazon”

Expires soon Amazon

ML Scientist

  • Seattle (King)
  • IT development

Job description



DESCRIPTION

Our vision at Amazon Comprehend Medical is to simplify and advance Natural Language Processing (NLP) in Healthcare. We are building a team of passionate people who are focused on changing the world for the better. Our mission is to provide a delightful experience to Amazon's customers by pushing the envelope in Automatic Speech Recognition (ASR), Machine Translation (MT), Natural Language Understanding (NLU), Machine Learning (ML). Since our launch, research has exploded in multiple directions such as Ontology Linking(ICD, Snomed, RxNorm), Radiology, Pathology, Temporality, Medical Conversational NLP, Summarization, multi-task and transfer learning, continual learning(catastrophic forgetting), translation and a variety of other topics.
If you want your work to positively impact the lives of millions of people and you're up for a challenge, let's talk!

We are looking for a passionate, talented, and inventive Senior Applied Scientists who can bring bleeding edge machine learning and NLP techniques into real products solving real problems together with a highly multi-disciplinary team of scientist, engineers, strategic partners, product managers and subject domain experts.

Desired profile



BASIC QUALIFICATIONS

PhD in machine learning (or in a highly related area) or equivalent experience · 2+ years of relevant, broad research experience after PhD degree or equivalent. · Solid background in statistical learning techniques for NLP (Deep Learning, HMMs, CRFs, Pretraining, Multi-task Learning, LSI, MRFs etc) as well as latest state-of-art-systems · Fluency with at least one of the modern distributed ML frameworks such as TensorFlow, PyTorch, MxNet. · Must have ML/NLP algorithm implementation experience as well as the ability to modify standard algorithms (e.g. change objectives, work-out the math and implement) · Experience and/or solid background on modern deep learning approaches to NLP: word/paragraph embedding, pretraining, representation learning, text/sentiment classification, ambiguity disambiguation, few shot learning.

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