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Machine Learning at LinkedIn
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Product. Engineering. Machine Learning. Natural Language Processing. Software Development. Data Science.
Carnegie Mellon University
Master of Science in Computer Science, Computer Science
January 1, 2016 – January 1, 2017
Indian Institute of Technology, Madras
BTech and MTech (dual degree), Electrical Engineering
January 1, 2009 – January 1, 2014
Senior Staff Software Engineer, Machine Learning
September 1, 2024 – Present
Staff Software Engineer, Machine Learning
March 1, 2022 – August 1, 2024
Senior Software Engineer, Machine Learning
March 1, 2020 – February 1, 2022
Machine Learning Engineer
April 1, 2018 – March 1, 2020
Machine Learning and Relevance Engineer Intern
May 1, 2017 – August 1, 2017
San Francisco Bay Area
Carnegie Mellon University
Graduate Teaching Assistant
January 1, 2017 – May 1, 2017
Greater Pittsburgh Area
Big Data Labs, American Express
Research Engineer
July 1, 2014 – July 1, 2016
Bangalore, India
American Express
Summer Internship
May 1, 2013 – July 1, 2013
Big Data Labs, Bangalore
Qualcomm
Summer Internship
May 1, 2012 – July 1, 2012
Hyderabad Area, India
Texas Instruments
Summer Internship
May 1, 2011 – July 1, 2011
Bengaluru Area, India
Dense Subtensor Mining
January 1, 2017 – May 1, 2017
Implemented D-cube algorithm to detect top-k dense blocks in tensors. Analyzed the network and fraudulent attacks on real-world datasets like DARPA network connections, Amazon and Yelp product review datasets through the dense blocks detected.
Parallelizing Pretraining of Deep Neural Networks using Stacked Autoencoders
January 1, 2017 – May 1, 2017
Implemented and analyzed data parallel and model parallel approaches to pretraining a deep neural network using stacked autoencoders. Developed generic and optimized multi-GPU implementations of pretraining for both data parallel and model parallel approaches in Tensorflow. For more information, please visit https://parapret.github.io/ppdnn/
Show, Demand and Tell - Goal Directed Image Captioning
January 1, 2017 – May 1, 2017
Worked on Image captioning problem given an image and a target demand word. Explored attention models for combining visual and language modality.
Face Swapping
September 1, 2016 – December 1, 2016
Implemented face swap using face detection and homography
Prediction of Next word given speech signal and associated emotions
September 1, 2016 – December 1, 2016
Joint modeling of speech signal and associated emotions to predict next spoken word using an LSTM architecture
Dual Degree Project: Modeling of glitching effects in estimation of Dynamic Power consumption
July 1, 2013 – June 1, 2014
• Identified patterns in glitch power consumption of circuits with process variations through Monte Carlo simulations • Implemented an ambiguity interval based algorithm to estimate bounds on power consumption • Implemented a probabilistic model to identify high glitch nodes in a circuit • Devised an approach to estimate power saving by blocking glitch propagation at a node and validated the approach by introducing latches at those nodes
Centralised inverter for Variable Frequency Drive
November 1, 2012 – December 1, 2012
- Modeled a technique to install Variable Frequency Drive efficiently for house-hold motor-based appliances using centralised inverter - Presented poster demonstrating reduction in AC-DC conversion losses
Smart Switchboard
November 1, 2012 – December 1, 2012
• Developed a low-cost versatile smart switchboard for curbing power losses in household appliances due to human negligence at 50% of the conventional cost • Implemented automatic switch-off and power monitoring features with a timer and LCD display
Reproducible Research
Coursera
June 24, 2026 – Present
Statistical Inference
Coursera
June 24, 2026 – Present
Developing Data Products
Coursera
June 24, 2026 – Present
Social Network Analysis
Coursera Course Certificates
June 24, 2026 – Present
Mining Massive Datasets
Coursera
June 24, 2026 – Present
R Programming
Coursera Course Certificates
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's career trajectory is heavily focused on Machine Learning and Software Engineering, with a strong emphasis on research and development. While there are transferable analytical skills, the target role of 'Data Analyst' might be a step down in terms of technical depth and scope compared to their 'Senior Staff Software Engineer, Machine Learning' experience. The projects are diverse in ML/AI but less focused on traditional data analysis, reporting, or business intelligence. This could indicate a potential mismatch if the Data Analyst role is not heavily focused on advanced ML/statistical modeling.
Soft Skills & Operational Fit
The candidate's extensive project history and progression through senior roles at LinkedIn suggest strong problem-solving, analytical thinking, and leadership skills. The role as a Graduate Teaching Assistant also indicates communication and mentorship abilities. However, without specific psychometric test results, a detailed assessment of work attitude, stress handling, and team collaboration is not possible.