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Machine Learning at Meta | ex-Apple
Currently, Kapil is working as a Tech lead for the Watch Feed Ranking for Meta. The team is responsible for generating the most relevant recommendation for 1B users. My current responsibilities include setting the technical direction for the team, taking active part in hiring, mentoring junior and new members in the team and driving product top-line impact using various modeling and retrieval techniques in recommendation stack. Previously, Kapil was working as a Research Engineer/ Data Scientist in the Maps Search team here at Apple. Prior to Apple, he was working in the Data Discovery/Data science team at Eventbrite. He was working on various big data and machine learning problems including recommendations and classification of events. He was also working on to improve the search relevancy of the results. His interests lies in applying Machine learning, Natural language and Information retrieval methodologies on big data by leveraging the power of Hadoop and see himself working as a Data Scientist in the future. He graduated from the Computer Science department of The Johns Hopkins University. He is using various Machine learning and Deep learning techniques to solve various problems in Search. Specialties: SOLR, Lucene, Cassandra, HBase Hadoop, Hive, Natural Language Processing, NLP, Information retrieval, Machine learning, Java, Python, Big data, Recommendations. Scikit Learn, Neo4j, Gephi
Udacity
Self Driving Car Nano Degree Term 1, Self Driving Car Nano Degree
January 1, 2017 – January 1, 2017
Punjab Engineering College
B.E., Electrical and Electronics Engineering
N/A – Present
The Johns Hopkins University
MS, Computer Science
N/A – Present
Meta
Tech Lead | Software Engineer, Machine Learning at Meta
May 1, 2018 – Present
Menlo Park, CA · On-site
Apple
Senior Research Engineer Machine learning
July 1, 2014 – May 1, 2018
Cupertino
Eventbrite
Big Data, Search and Data Science Engineer
July 1, 2012 – July 1, 2014
San Francisco Bay Area
AT&T Interactive [YellowPages.com]
Senior Software Engineer with Data Insight Team
January 1, 2011 – July 1, 2012
San Francisco Bay Area
Pipio
Software Engineer for Search
December 1, 2009 – September 1, 2010
Aleph Point
Software Intern at Aleph Point
June 1, 2009 – February 1, 2010
Center for language and speech processing
Team member at CLSP'09 workshop
June 1, 2009 – July 1, 2009
Kaggle Completion: StumbleUpon Evergreen Classification Challenge
October 1, 2013 – Present
Worked on a Kaggle Machine learning Competition. Build a classifier to categorize webpages as evergreen or non-evergreen https://www.kaggle.com/c/stumbleupon https://github.com/kapild/kaggle/tree/master/stumble-evergreen
Kaggle Yelp Recsys 2013
August 1, 2013 – Present
My first attempt to a Kaggle competition. To predict Yelp business ratings. I use libFM library https://www.kaggle.com/c/yelp-recsys-2013/
Rdio + Google Chrome plugin
March 1, 2012 – April 1, 2012
Description One click (for Rdio®) way of adding your currently playing Rdio songs to your Rdio playlist. One Click Add. OneClick+ OneClick+: It's a Google chrome extension to provide an one click solution of adding your currently playing songs to your Rdio playlist. OneClick+ works with Rdio®. Overview -------- This extension uses the 3-legged Oauth authentication to your Rdio account. After doing the initial handshake of exchanging tokens, it loads your owned playlist and the most recently played song. One can just click on the '+' link against a playlist name to add that song to that playlist. It's an one click solution to add your currently playing song to one of your playlist. a) It is useful for people who listens to lot of recommendations and would like to easily add the song they are currently listening to a playlist of their choice. b) You have an option to create a new playlist and add the new song to it. c) You can search for songs, and it will return the most common track(top hit) for that search query. You can then again add the song to the playlist of your choice or add another new playlist.
ToBikeToBart
February 1, 2012 – Present
ToBikeToBart: This is a very simple android app, which uses BART API. The purpose of the app is to tell user whether he is allowed to carry his bike to bart or not. The input is arrival and departure station, along with a specific time he/she wish to travel. Then the app calls the BART API, stating whether he can carry his bike on the following trains. I face this problem when I am biking to/back from work. At times I dont wanna bike and would like to BART with bike instead. Hence, ToBikeToBart Other funny names : ToBOrNotToBToB: TO bike or not to bike to BART BART API: http://api.bart.gov
Cloudera Certified Hadoop Developer
Cloudera
June 24, 2026 – Present
Machine Learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's career progression through major tech companies (Meta, Apple, Eventbrite, AT&T Interactive) demonstrates adaptability and a drive for continuous learning and impact. Their involvement in Kaggle competitions and personal projects indicates a proactive, self-motivated individual. The breadth of experience across search, recommendations, and core ML aligns well with dynamic, innovation-driven environments.
Soft Skills & Operational Fit
The candidate's extensive experience as a Tech Lead at Meta and Senior Research Engineer at Apple suggests strong leadership, problem-solving, and project ownership skills. Their work on end-to-end systems implies a robust operational fit for complex ML engineering roles.