Machine Learning Scientist

Job ID: 1679533 | Amazon.com Services LLC

DESCRIPTION

Do you want to apply machine learning to a completely new field for Amazon? Our team, part of Amazon Halo, is launching a new ambitious project dedicated to applying ML techniques to genomic & immunological data to help make people's lives better. Datasets will come from our own experiments. This is a chance to get in on the ground floor of an exciting new initiative.

Job responsibilities
· Develop machine learning models & algorithms on genomics & immunological data.
· Analyze scientific data to validate ML models & derive insights.
· Communicate scientific findings internally & externally.


Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

BASIC QUALIFICATIONS

· PhD or equivalent Master's Degree plus 4+ years of experience in CS, CE, ML or related field
· 2+ years of experience of building machine learning models for business application
· Experience programming in Java, C++, Python or related language

PREFERRED QUALIFICATIONS

· Experience with immunological or genomics data
· Experience with deep learning, particularly self-supervised sequence modeling
· Experience with ML applications for scientific problems, e.g., bioinformatics, protein modeling, or biochemistry
· Track-record of novel algorithm development, with publications in leading journals or conferences.
· Algorithm implementation experience as well as the ability to modify standard algorithms (e.g. changing objectives, working-out the math, implementing and scaling)
· Experience with AWS computing infrastructure for machine learning
· Strong programming skills in at least one object oriented programming language (preferrably Python) and experience with common deep learning libraries, such as PyTorch, Keras, or Tensorflow.