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Machine Learning Infrastructure - Engineering Manager - Netflix

Netflix


Location:
Los Gatos, California
Date:
03/02/2017
Categories:
  • Data Analytics
  • Data Engineer
  • Data Scientist
Netflix
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Job Details

Netflix is driven by data and algorithms (along with tons of great TV shows and movies). If you are in the engineering, data, and / or machine learning field, this is an amazing place to be.

We are building out a new machine learning infrastructure team and we’d like you to lead it.

The mandate for the ML infrastructure team is to envision, develop, and manage the systems / workflows that enable Netflix data scientists to focus more on algorithms and analyses and less on the associated engineering.

 

Some Things For Your Team To Tackle Include

  • How to best take a prototype in R or Python and move it into ongoing production use (at scale)?
  • How to build flexible pipelines that we can rapidly evolve to handle new technologies and modeling approaches?
  • How to streamline feature engineering, such that the underlying data is easily and efficiently extracted and the engineered features are reusable across analyses?
  • How to better enable data science research projects to be collaborative and reproducible?

     

Help us figure this out and make it happen, in partnership with your data scientist customers and your peer engineering infrastructure teams.

This is just the tip of the iceberg, though. Enablement of our data scientists through infrastructure development and smoothing is a relatively green field for you to gather and address needs, upleveling requests into generalized solutions.

Given Netflix’s rare combination of an exceptional team of data scientists, a lot of data, a sophisticated data engineering ecosystem, and a company-wide understanding and appreciation for the benefits of machine learning, a huge opportunity for impact awaits!

Requirements

Key Traits Needed For This Role

  • Expertise in the machine learning engineering field
  • Visionary
  • Comfortable and capable leader
  • Can drive cross-functional projects forward
  • Strength in execution
  • Listens / curious / adaptable / collaborative