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Sr. Machine Learning Engineer at Spotify
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Working on making Ads serve intelligently on Twitter
Carnegie Mellon University
Master's degree, Artificial Intelligence
January 1, 2017 – January 1, 2018
Vellore Institute of Technology
Bachelor's degree, Computer Science
January 1, 2011 – January 1, 2015
Kishinchand Chellaram College of Arts Commerce and Science
H.S.C., Science
January 1, 2009 – January 1, 2011
Spotify
Senior Machine Learning Engineer
January 1, 2023 – Present
San Francisco, California, United States · Remote
Sr. Machine Learning Engineer
September 1, 2021 – December 1, 2022
Machine Learning Engineer II
August 1, 2020 – September 1, 2021
Machine Learning Engineer
February 1, 2019 – August 1, 2020
Carnegie Mellon University
Graduate Teaching Assistant
July 1, 2018 – December 1, 2018
Greater Pittsburgh Region
C3 IoT
Machine Learning Engineer Intern
May 1, 2018 – August 1, 2018
Redwood city
Carnegie Mellon University
Masters Student in Machine Learning
August 1, 2017 – December 1, 2018
Greater Pittsburgh Region
Carnegie Mellon University - School of Computer Science - Language Technologies Institute
Graduate Student Researcher
August 1, 2017 – May 1, 2018
Greater Pittsburgh Region
Qatar Computing Research Institute
Research Intern
May 1, 2017 – August 1, 2017
Doha, Doha, Qatar
PayPal
Software Developer
July 1, 2015 – May 1, 2017
PayPal
Intern
January 1, 2015 – June 1, 2015
S&P Capital IQ
Summer Intern
June 1, 2014 – July 1, 2014
Hyderabad, Telangana, India
Cross-modal Audio to Visual Generation using Generative Adversarial Networks (GANs)
March 1, 2018 – May 1, 2018
GAN based algorithm trained on kinetics video dataset to generate an image of the source of an audio based on the correlation learnt between the audio and the image in the videos.
Goal Generation for Hindsight Experience Replay for Reinforcement Learning
January 1, 2018 – May 1, 2018
Theorized, implemented and evaluated various metric based prioritization techniques for the experience that must be replayed to enable the RL agent to learn quickly from desirable as well as undesirable experiences.
Search Engine
August 1, 2017 – November 1, 2017
Built a Lucene based search engine as a part of a course requirement coupled with a myriad of information retrieval algorithms like exact-match Unranked Boolean, Ranked Boolean and best-match BM25 and Indri algorithms. Implemented result re-ranking techniques like query expansion, Learning to Rank (LeToR) and query diversification. Evaluated performance based on metrics like MAP (Mean Average Precision), Precision@k and NDCG (Normalized Discounted Cumulative Gain)
EcoKitchen
January 1, 2015 – Present
Collaboratively built a compound web and mobile donation system that enabled donors that enabled bidirectional operations between the donor and the recipient
Clustering Models
January 1, 2014 – Present
Conducted a comparative study of different clustering algorithms by modelling them in Java and using different indexes
Ovuguide
January 1, 2013 – Present
Built an Android health care application for MIT's healthcare initiative SANA in order to prevent unwanted pregnancy by tracking and predicting the ovulatory cycle based on a simple rule-based prediction algorithm
Cultural Fit Analysis
The candidate's career progression from Software Developer to Senior ML Engineer at prominent tech companies (PayPal, Twitter, Spotify) demonstrates ambition and adaptability. Their involvement in diverse projects, from academic research to industry-leading ML systems, indicates a broad interest in technology and a willingness to tackle varied challenges. The focus on impactful, revenue-generating projects (e.g., 15% revenue increase at Spotify) suggests a results-oriented mindset. The academic background from CMU and teaching assistant role also points to a continuous learning and knowledge-sharing culture fit.
Soft Skills & Operational Fit
The candidate's experience as a Senior ML Engineer at Spotify and Twitter, along with their role as a Graduate Teaching Assistant, suggests strong leadership, collaboration, and communication skills. Their work on complex, end-to-end ML systems indicates a robust problem-solving approach and operational acumen. The description of contributing to a 'scalable feature store' and 'designing the technical roadmap' points to strategic thinking and cross-functional collaboration.