Machine Learning Engineer
🇺🇸 United States › New York › New York City (Posted Jun 5 2018)
About the company
Founded by an experienced team of serial entrepreneurs from successful startups (Uber, Teladoc, Gilt, One Medical Group, Buzzfeed), we are passionate about improving access to high quality and personal health care.
From long days of perfecting a great product and long nights of happy hours or group dinners, we are looking for someone smart, energetic, and fun to join our tight-knit and growing team.
A passion for health care is not necessary to apply; however, a passion for improving the lives of people and living a better life through technology is a must.
We are looking for a Machine Learning Engineer to build and deploy Machine Learning models to augment our clinicians and provide the best possible care to our patients.
ABOUT PAGER ENGINEERING
Decentralized in decision making
OUR TECH STACK
Node.js, Python, Go
Native iOS and Android
Mongo, Postgres, Redis
Jenkins, Docker, AWS, Google Cloud
Skills & requirements
2+ years of experience in a production machine learning environment: You can Define, Prototype, Develop, Deploy, and Monitor ML Models
4+ yrs of experience in software engineering, building and maintaining APIs and production level systems
Professional command of Python (Numpy, Scipy, Pandas, Scikit-learn)
M.Sc. or Ph.D. in computer science, engineering, statistics, computational linguistics, or other quantitative field or relevant equivalent professional experience
NICE TO HAVES
Work experience in Healthcare
Hands-on experience with natural language processing libraries including (not limited to) SpaCy, GloVe, …
Experience with high performance and distributed computing (Apache Spark or Apache Arrow).
Experience working in a fast-paced startup environment: You take responsibility for your projects and pride in your work.
Experience with neural networks for natural language processing
Experience dealing with health system partners data (e.g. EHRs, CCDA, FHIR, HIEs, ADT feeds and claims/pre-auth feeds)
Experience in using Tensorflow and Keras in production
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