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Senior Machine Learning Engineer at Apple | ex-LinkedIn
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Senior Machine Learning Engineer at Apple | ex-LinkedIn | MS in CS at Georgia Tech | BTech at IIT Guwahati | Interested in Artificial Intelligence, Machine Learning, Software Engineering, Distributed Systems, and Application Development
Georgia Institute of Technology
Master’s Degree, Computer Science
N/A – Present
Indian Institute of Technology, Guwahati
Bachelor’s Degree, Mathematics and Computing
N/A – Present
Apple
Senior Machine Learning Engineer
January 1, 2020 – Present
Cupertino, California, United States
Senior Software Engineer, Machine Learning
January 1, 2017 – January 1, 2020
Mountain View, California, United States
Bloomreach
Software Engineer
February 1, 2016 – August 1, 2017
Mountain View, CA
Georgia Institute of Technology
Graduate Researcher
August 1, 2015 – December 1, 2015
Atlanta Metropolitan Area
BloomReach
Software Engineer
May 1, 2015 – August 1, 2015
Mountain View, CA
Georgia Institute of Technology
Graduate Teaching Assistant
January 1, 2015 – May 1, 2015
Atlanta Metropolitan Area
Institute of Statistical Mathematics
Researcher
April 1, 2013 – July 1, 2013
Tokyo, Japan
Indo-German Winter Academy
Speaker
December 1, 2012 – December 1, 2012
JAIST
Researcher
April 1, 2012 – July 1, 2012
Nomi, Ishikawa, Japan
Collaborative Filtering
January 1, 2015 – December 1, 2015
I have written three codes, one for user-based collaborative filtering, second for item-based collaborative filtering and the third for hybrid-based collaborative filtering. The project has 154 stars on Github as of May 2023.
Modeling Aspects, Ratings & Sentiments for Movie Recommender
January 1, 2015 – April 1, 2015
• Wrote python scripts to jointly model the Aspects, Ratings and Sentiments for Movie Recommendation using data from IMDb. • Achieved Mean Square Error of 4.97 on the held-out test data-set as measured by McNemar’s test. The project has 53 stars on Github as of May 2023.
Multifaceted Collaborative Filtering Model
January 1, 2015 – April 1, 2015
• Wrote python scripts to integrate baseline, item-based neighborhood and SVD++ models in a single combined model. • Applied 10-fold cross validation to choose the rank (number of latent factors), learning rate and regularization parameter. • Achieved Root Mean Square Error (RMSE) of 0.901 on the test data-set (using MovieLens Data-set).
Decision Tree
November 1, 2014 – December 1, 2014
• Implemented a Decision Tree using ID3 Heuristic for the given salary dataset in Python. • Applied 10-fold cross validation to obtain the mean accuracy of 85.2%.
SMS Spam Detection
October 1, 2014 – December 1, 2014
• Built binary classifiers to distinguish between legitimate and spam SMSes based on their text features. • Implemented classifiers using Naive Bayes with Laplace Smoothing and Support Vector Machine (SVM) with Linear Kernel. • Achieved mean accuracy of 98.79\% for Naive Bayes Classifier and 97.68\% for SVM Classifier.
Topic Modeling on Yelp Reviews
October 1, 2014 – December 1, 2014
• Applied Latent Dirichlet Allocation (LDA) to Yelp Reviews to extract specific topics from them. • Predicted ratings of extracted topics for each review by applying Multi-Aspect Sentiment Analysis, instead of overall rating of reviews.
Recommendation System
October 1, 2014 – November 1, 2014
• Implemented a Recommender System using Low-Rank Matrix Factorization and Gradient Descent in Matlab. • Achieved Root Mean Square Error (RMSE) of 0.93 on the test data-set.
Image Compression
September 1, 2014 – October 1, 2014
• Given a RGB bitmap image file, clustered the pixels using algorithms – K-means & K-medoids in Matlab. • In K-medoids clustering, Manhattan Distance and Partitioning Around Medoids (PAM) algorithm were used. • Created the final compressed image by coloring each cluster with the representative color of it’s centroid.
eBay Discovery (HackGT)
September 1, 2014 – September 1, 2014
Entry on Challenge Post: http://hackgt2014.challengepost.com/submissions/27079-ebay-discovery • Built a web interface to play and interact with eBay APIs. • Used eBay’s Finding API and Shopping API to extract details about sellers and items searched by the buyers. • Wrote Python scripts to parse the data obtained in JSON format which was later used by the UI Team. • Used D3.js to make visualizations for Price Trends Analysis of items returned by the keyword search.
Collecting and Visualizing Last.fm Data
August 1, 2014 – September 1, 2014
• Used the Last.fm API to download data about music tracks, and 100 similar tracks for each track. • Wrote Python Scripts to create and clean the data files using the downloaded Last.fm data. • Used the tool Gephi to visualize the data as an undirected graph.
Building a Search Engine
June 1, 2014 – July 1, 2014
• Learnt to build a search engine using Python by taking a course on Udacity. • Implemented key search engine components including a Crawler, an Index and a Page Rank Algorithm.
Machine Translation System using Translation Memory
January 1, 2014 – March 1, 2014
• Implemented a Machine Translation System using Translation Memory. • Found close matches from Translation Memory using Edit-Based Distance. • Worked on Target-Language Edit Hints.
Generating the Initials of a Signature
November 1, 2012 – December 1, 2012
• Implemented Cubic Spline Interpolation Technique to generate the initials of a Signature.
Duolingo Spanish Fluency: Elementary (Estimated)
Duolingo
June 24, 2026 – Present
Youth Empowerment and Skills (YES!+)
The Art of Living
June 24, 2026 – Present
The Data Scientist’s Toolbox
Coursera
June 24, 2026 – Present
Creative, Serious and Playful Science of Android Apps
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio is heavily skewed towards Machine Learning and Data Science, which aligns well with roles requiring strong analytical and algorithmic skills. The experience at Apple and LinkedIn in AI/ML organizations suggests a fit for fast-paced, innovation-driven environments. However, the target role is 'Backend Engineer', and while ML often involves backend components, the resume doesn't explicitly highlight traditional backend engineering skills (e.g., distributed systems, API design, database optimization beyond ML data processing). This might indicate a slight mismatch if the backend role is not ML-focused. The diversity of personal projects showcases initiative and continuous learning.
Soft Skills & Operational Fit
The candidate's resume indicates a strong focus on research and complex problem-solving, suggesting a detail-oriented and analytical approach. Experience as a Graduate Teaching Assistant implies communication and mentorship skills. However, without psychometric test results or interview data, a comprehensive assessment of soft skills and operational fit is limited.