Research Lead @ Gemini Post-Training | ex-Meta GenAI and Reality Labs, ex-Baidu | CMU, LTI
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Driven and passionate machine learning engineer with 8+ years of ML and Deep Learning, and 10 years of software engineering experience. 4+ years of technical lead experience, leading team of engineers, computational linguists and scientists
Carnegie Mellon University
Master of Computational Data Science, Analytics
January 1, 2015 – December 1, 2016
Netaji Subhas Institute of Technology
B.E, Information Technology
January 1, 2008 – January 1, 2012
Ryans International School
Class 12, Class 12
January 1, 2006 – January 1, 2008
Air Force Golden Jubilee Institute
Class 10, Class 10
January 1, 2001 – January 1, 2006
Staff Research Engineer
December 1, 2023 – Present
Mountain View, California, United States
Meta
Tech Lead Software Engineer, Machine Learning
May 1, 2018 – November 1, 2023
Menlo Park, California
Baidu USA
Machine Learning Engineer
February 1, 2017 – May 1, 2018
Sunnyvale, California
Pure Storage
Data Science Intern
May 1, 2016 – August 1, 2016
Mountain View, California
Carnegie Mellon University
Graduate Teaching Assistant
January 1, 2016 – May 1, 2016
Pittsburgh, Pennsylvania · On-site
Adobe
Senior Member of Technical Staff
February 1, 2014 – July 1, 2015
Noida, Uttar Pradesh, India
Adobe
Member of Technical Staff
July 1, 2012 – February 1, 2014
Noida, Uttar Pradesh, India
NSITonline.in
Database and Server Administrator
June 1, 2009 – June 1, 2010
SEETA
Software Engineer, Intern
June 1, 2009 – December 1, 2010
Greater Delhi Area
Predicting Business Ratings on Yelp Dataset
November 1, 2015 – December 1, 2015
Developed a text-mining based machine learning system to predict the user ratings of a business using the review text. Trained Logistic Regression and SVM models using various language based feature engineering techniques. The model that gave the best accuracy was chosen through cross-validation.
Collaborative Filtering for Movie Recommendations
October 1, 2015 – Present
Worked on using collaborative filtering to develop a movie recommendation system for users on the given Netflix Prize Dataset. Implemented memory-based method & model-based methods to predict the ratings of movies for new users. Used Pearson's Correlation Coefficient (PCC) to account for user and movie bias in the ratings.In addition, performed bipartite clustering to compute user and movie clusters.
Text based Information Retrieval on ClueWeb09 dataset
September 1, 2015 – December 1, 2015
Built a search engine in Java to retrieve and rank documents using boolean, BM25 and Indri retrieval models. Implemented LETOR re-ranking(SVM-rank) and query expansion by pseudo-relevance feedback for better results. Conducted experiments to evaluate search quality using metrics like Mean Average Precision and Precision@N. Technologies Used: Java, Lucene
Bipartite clustering on news articles from TDT4 Dataset
September 1, 2015 – November 1, 2015
Implemented a reinforcement Bipartite Clustering algorithm on news articles from the TDT4 Dataset. The algorithm produces both document clusters and word clusters simultaneously. Algorithm was evaluated using both external metrics (F-1 Score) and internal metrics (sum of cosine similarities of documents to the corresponding centroid).
General Purpose Dynamic Memory Allocator
July 1, 2015 – Present
Implemented a general purpose dynamic memory allocator in C - malloc( ), free( ), calloc( ) and realloc( ). Built using segregated lists with a first fit algorithm and constant coalescing time.
Model Based Testing from Sequence Diagrams using Genetic Algorithms
December 1, 2011 – May 1, 2012
The following 3 objectives were accomplished:- 1. Generation of Test Cases from UML 2.0 Sequence Diagrams. 2. Prioritization of Test Cases using Genetic Algorithm. 3. A GUI based tool was designed in C++ which generated the Test Suite from a Sequence Diagram
Online Placement Portal, NSIT
May 1, 2010 – July 1, 2010
Lead Developer of the PHP based Web Placement Portal (with 3 other team members) to facilitate the strenuous task of Training and Placements in NSIT. It is being used by the Training and Placement Department of NSIT (http://tnp.nsitonline.in) since August 2010. The basic objective was the elimination of paperwork for the placement procedure. It uses PHP, HTML, CSS, JavaScript, JQuery, Ajax & MySQL technologies. Implemented various features in the above technologies to provide a User Friendly system.
Cultural Fit Analysis
The candidate has a diverse project portfolio ranging from low-level memory management to advanced machine learning, indicating adaptability and a broad interest in computer science. Their experience at multiple large tech companies (Google, Meta, Baidu, Adobe) suggests an ability to thrive in fast-paced, high-performance environments. The progression into leadership and research roles aligns with a growth mindset. The target role of ML Engineer aligns well with their recent experience and academic background.
Soft Skills & Operational Fit
The candidate's experience as a Tech Lead and Research Lead at top-tier companies like Meta and Google suggests strong leadership, problem-solving, and collaboration skills. Their role as a Graduate Teaching Assistant also indicates an ability to communicate complex technical concepts. The project descriptions are clear and demonstrate a structured approach to problem-solving.