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What a Software Engineer does at Google Display Ads and Google Brain?

This is an example of what a Software Engineer does at Google Display Ads and Google Brain.

Got it from https://www.linkedin.com/in/cshallue/
Christopher Shallue, senior software engineer at Google AI in Mountain View, California

Software Engineer
Google Display Ads

Summary:
• Software Engineer on the Google Display Ads team, primarily focused on GMail and Google Maps.
• Specialized in machine learning models for ad selection and personalization.

Prediction modeling:
• Lead engineer developing large-scale logistic regression models for ad selection (billions of training examples, tens of millions of weights, highly parallelized).
• Responsible for end-to-end development including model training, evaluation, experiments and launch.
• Oversaw and implemented significant improvements to the training data collection pipeline, increasing both data quality and quantity.
• 5 launches with combined revenue impact $30+ million / year.

Content recommendation:
• Lead engineer developing a large-scale collaborative filtering model for content recommendation (hundreds of millions of examples, highly parallelized).
• Owned and drove this project through complete end-to-end development including design, model building, infrastructure changes, experiments and launch.
• Launched with revenue impact $5+ million / year.

Leadership:
• Technical Lead (TL) of Modeling Team (9 members): ran a weekly meeting and managed the agenda; consulted and advised for numerous machine learning projects within the team.
• Mentored several new team members.

Senior Software Engineer
Google Brain

Summary:

• I am a senior research software engineer on the Google Brain team (http://g.co/brain).
• I work on a variety of machine learning research projects. My role for each project is one (or more) of: research lead, technical lead, mentor and/or collaborator.
• I teach a 2-day machine learning class at Google offices around the world – so far I’ve taught in Mountain View, New York and Zurich.

Selected projects highlighted below.

Identifying exoplanets with deep learning:
• Can we use deep learning to discover new planets? I posed this question and initiated a collaboration with an astronomer at Harvard to find out.
• As project lead, I developed convolutional neural networks to identify exoplanets in data from NASA’s Kepler mission.
• I gave a guest lecture on this project at the NASA Frontier Development Lab.
• Publication and code coming soon!

Unsupervised sentence embeddings:
• I implemented and published the Skip Thoughts model in TensorFlow.
• Using synchronized distributed training, I improved the training time from 4 weeks to 4 days with no loss of accuracy.

Machine learning for image captioning:
• I developed the latest version of Google’s machine learning system that automatically produces descriptive image captions. This work combines deep neural networks for computer vision and machine translation.
• Compared to the previous version, my implementation has 4x faster training speed and achieves an additional 2 points of accuracy in the BLEU-4 metric.
• I wrote a widely-read article on Google’s Research Blog.

Killing Joke – Eighties

 

 

If you pretend to know about this song / Nirvana – Came as you are….

Here are the four songs that have a very similar sound to “Come As You Are” with YouTube links included. You can hear the common elements in each tune, but all the tracks are quite good and unique in their own way.

  1. 22 Faces”- Garden of Delight

  2. Baby Come Back”- The Royals

  3. Eighties”- Killing Joke

  4. Life Goes On”- The Damned

View story at Medium.com