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GenAI Leadership - Media Search & Personalization @ Apple
As a Machine Learning Manager at Apple, I’ve had the opportunity to lead and transform key projects in Music & Podcast Search, driving technological advancements that enhance user experiences across Apple’s ecosystem. My expertise lies in search engineering, information retrieval, and leveraging emerging technologies to push the boundaries of product innovation. In my current role, I lead the Apple Music and Podcast Search Team, where I’ve spearheaded an innovative team responsible for features and quality of search across apple's search ecosystem. These projects have not only strengthened our team’s capabilities but also introduced new approaches to search and personalization, resulting in patents that have set new benchmarks for the industry. My leadership is defined by a commitment to innovation, cross-functional collaboration, and operational excellence. I’ve successfully fostered partnerships with teams across Apple leading to the deployment of features that significantly enhance the Apple ecosystem. Mentorship and team development are core to my approach. I’ve guided engineers in building architectures and established new areas driving long-term improvements and helping develop future leaders within the organization.
University of Southern California
Master of Science (MS), Computer Science
January 1, 2013 – January 1, 2015
National Institute of Technology Karnataka
Bachelor of Technology (BTech), Computer Science
January 1, 2007 – January 1, 2011
Apple
Machine learning Manager 3
October 1, 2021 – Present
Apple
Software Engineer 3
February 1, 2015 – Present
USC Institute for Creative Technologies
Artificial Intelligence Student Programmer
August 1, 2014 – December 1, 2014
Playa Vista, CA
Expedia, Inc.
Software Developer Intern
May 1, 2014 – August 1, 2014
San Francisco Bay Area
National Instruments
Software Technical Lead
March 1, 2013 – July 1, 2013
Bengaluru, Karnataka, India
National Instruments
Software Engineer
July 1, 2011 – July 1, 2013
Bengaluru, Karnataka, India
National Institute of Technology Karnataka, Surathkal
Convener - Student Head - Incident
May 1, 2010 – May 1, 2011
Bengaluru, Karnataka, India
IBM India Software Labs
Software Development Intern
April 1, 2010 – July 1, 2010
Bengaluru, Karnataka, India
Indian Institute of Science
Software Engineer Research Intern
December 1, 2009 – January 1, 2010
Bengaluru, Karnataka, India
Twitter Hashtag Source Finder
May 1, 2014 – Present
Developed a summarizer tool to tell users why a specific hashtag is trending.Hashhtags are very cryptic most of the times and figuring out what hashtags are referring to is a problem in itself. Summary is a one liner which says exactly why someone or something is trending. Developed a system to help users find why a specific hashtag is trending using NLP techniques : - Keyword Extraction in tweets - Information Retrieval (searching for articles which talk about the keywords extracted) - Summarizing document with maximum match
Error detection and correction: dealing with homophone confusion
April 1, 2014 – Present
Developed a tool which detects and corrects spelling errors caused by confusion in words that sound similar but which are spelled differently (homophones). In written language, errors involving the use of words that sound similar or the same are fairly common. For example, the word its is frequently used where it's should, and vice-versa. Other confusable pairs include: they're/their/there, you're/your and loose/lose.
A game theoretic approach to include randomness in recommender system
March 1, 2014 – April 1, 2014
Research on including randomness to the Collaborative Filtering Recommendation algorithm in such a way that the total utility of the system (both user and the website) remains the same. This was modeled using a Game theory model in which the user and the website are playing a game trying to maximize their own profits. Built a recommender system using Apache Mahout by collecting movie interests/ratings data from the users. The information was then used by a Game theory engine to recommend movies to the users. The efficiency of the system was calculated by the number of clicks by the user and the money made by the website.
Text Clasification [Java]
January 1, 2014 – Present
Spam detection and Movie review sentiment analysis using Naïve Bayes and Perceptron learning algorithms
Android library to flood a table using a Json object [Java, Android]
December 1, 2013 – Present
An android library which uses a Json object to flood the table entries in a table layout.
Performance evaluation of hueristics on problem with constraints [Python],
October 1, 2013 – Present
Evaluated various heuristics (like MRV, MCV) for constraint satisfaction problems by building a Ken-Ken problem solver.
Android Application to give city weather forecast. [Java, Android]
October 1, 2013 – Present
A Web scrapper application to give weather information/forecast for a particular location (Zipcode/City).The application uses the Yahoo weather service API to get the weather information.
Pacman Game Engine [Python]
October 1, 2013 – Present
Built an AI engine to play pacman that uses probablistic inference technique to track the ghosts and avoid them.
Research on Memory Optimization
April 1, 2013 – July 1, 2013
Research on using memory more efficiently in C, LabVIEW and .NET APIs.
Network Switch Device APIs – National Instruments [C#]
March 1, 2013 – July 1, 2013
Network Switch Device APIs provides users the platform to test their Network Switches. My role was to design and develop .NET APIs which the customers could use and write a GUI application around the API demonstrating the usage.
Improving Usability of Code.
November 1, 2012 – December 1, 2012
Research on improving usability of code and thus user experience by following certain consistent guidelines in the naming conventions of APIs.
Research on calling LabVIEW and C from .NET
January 1, 2012 – May 1, 2012
Benchmark performance tests to check the performance of PInvoke calls to C and LabVIEW from .NET.
GPU enabled SNMP Trap Receiver.- Undergraduate Thesis- [Java]
August 1, 2010 – April 1, 2011
The objective of the project was to build a trap receiver with GPU capabilities.The tool was capable takes advantage of GPU’s and had processing capabilities to find the origin of fault in the network. It was built with intelligence to classify traps based on their severity.
SMART (Smart Miscompare Analysis Reporting Tool) [C]
April 1, 2010 – July 1, 2010
SMART is an Intelligent Debugging and reporting Tool which used supervised learning algorithm which made intelligent analysis in the various bug report file generated .
Intelligent Online Examination [Java, Jsp]
August 1, 2009 – December 1, 2009
An online examination software which focuses on providing intelligence by analyzing student performance during an online examination. The software had intelligence and analyzed user’s performance during the exam and determined the succeeding question based on it.
Building an Operating System [C, Assembly]
August 1, 2009 – December 1, 2009
Built a operating system which consisted of a boot loader and a microkernel. I was the only one out of a class of 72 to present a working Operating system built from scratch. I was guided by Dr. Annappa during the entire project.
Convolutional Neural Networks
Coursera
June 24, 2026 – Present
Structuring Machine Learning Projects
Coursera
June 24, 2026 – Present
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
Coursera
June 24, 2026 – Present
Sequence Models
Coursera
June 24, 2026 – Present
Neural Networks and Deep Learning
Coursera
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from operating systems to AI and NLP, indicates a broad technical curiosity and adaptability. Their experience at large companies like Apple and Expedia, combined with academic research roles, suggests an ability to thrive in structured and innovative environments. The target role of 'Big Data Engineer' aligns with their demonstrated interest in data processing, machine learning, and system optimization, although direct experience with specific Big Data technologies (e.g., Hadoop, Spark, Kafka) is not explicitly detailed in the provided project descriptions or skills.
Soft Skills & Operational Fit
The candidate's experience as a 'Convener - Student Head - Incident' and 'Software Technical Lead' suggests strong leadership, organizational, and project management skills. The descriptions of personal projects also indicate a proactive and problem-solving attitude. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is limited.