Kasturi Gottivedu Shriniwas

Software Engineer

Mountain View, California, United States5 yrs 4 mos experience

Most Likely To Switch

Key Highlights

Expert in developing AI-powered recommendation systems.
Strong background in backend development for e-commerce.
Proven experience in machine learning and deep learning applications.

Stackforce AI infers this person is a Backend-focused Software Engineer with expertise in AI and E-commerce.

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Skills

Core Skills

Machine LearningDeep LearningComputer VisionBackend Development

Other Skills

Natural Language UnderstandingPyTorchPython (Programming Language)C++JavaSpring MVCHTMLCSSNode.jsSAP HybrisC (Programming Language)

About

I am a Software Engineer at Google, currently working within the YouTube Ads team. My primary focus is on developing sophisticated recommendation models for youtube ads with special focus on user modelling and leveraging Large Language Models (LLMs) to build intelligent agents. Previously at Google, I contributed to Search infrastructure, where I worked on ensuring the reliability and stability of large-scale search models. My background bridges cutting-edge AI research and scalable engineering. I hold an MS in Computer Science from Georgia Tech, where I specialized in Machine Learning, and also worked as a researcher to study the application of Latent Diffusion models in image editing. Prior to Google, I spent two years as a Backend Developer at Walmart Labs, solving complex e-commerce challenges using Java and Spring MVC. I am passionate about using my skills in C++ and Python to solve algorithmic problems at scale.

Experience

Google

Software Engineer, ML

Jun 2023 – Present · 2 yrs 9 mos · Mountain View, California, United States · On-site

Youtube Ads foundation models:
contributing to AI powered Youtube Ads
Search Models Excellence:
Contributed to the infrastructure for reliable model releases

Deep LearningMachine LearningNatural Language Understanding

Georgia institute of technology

3 roles

Research Assistant

Promoted

Jan 2023 – May 2023 · 4 mos · United States · On-site

Worked on creating interpretable controls for image synthesis using latent diffusion models. Identified important latent directions based on PCA applied in latent space.

PyTorchDeep LearningComputer VisionNatural Language Understanding

Graduate Teaching Assistant

Aug 2022 – Dec 2022 · 4 mos · United States · On-site

Teaching Assistant for the Machine Learning course at Gatech CS7641/4641

Python (Programming Language)Machine Learning

Graduate Teaching Assistant

Jan 2022 – Jun 2022 · 5 mos · Atlanta, Georgia, United States

Teaching Assistant for the course CS6515-Grad Algorithms (Advanced Algorithms course) at Georgia Tech.

B garage

Computer Vision Engineering Intern

Jan 2023 – May 2023 · 4 mos · United States · Remote

Google

Software Engineer Intern

Jun 2022 – Aug 2022 · 2 mos · Los Angeles, California, United States

Working with the Google Assistant Team

C++Python (Programming Language)

Walmart labs

Software Engineer 2

Aug 2019 – Jul 2021 · 1 yr 11 mos · Greater Bengaluru Area

I worked as a backend developer for the e-commerce channel of "Makro"- Walmart, South Africa. I worked on challenging problems such as designing the code flow for managing stock for promotional products during the flash sale and contributed to various crucial features like integration with the marketing cloud. I have used web technologies- spring MVC (HYBRIS), HTML, CSS, JSP. Using these technologies, I also worked with Walmart India on the "Best Price" website, developing various features.

JavaSpring MVCHTMLCSSBackend Development

Indian institute of technology, kanpur

Research Intern

Dec 2018 – Feb 2019 · 2 mos · Greater Lucknow Area

I worked with the computer vision and language division on the Visual Question Answering Task. I leveraged PyTorch to implement various VQA frameworks such as Stacked Attention Network (SAN) & VQA Counting and visualized the attention maps generated by these techniques. Conclusively, I devised an efficient algorithm to improve the attention mechanism, which in turn increased the prediction accuracy of a given VQA framework (~ 2% for SAN). I continued my research work with IITK, and along with my team, I succeeded in generalizing the devised approach to any attention-based deep learning task and achieved a significant improvement over baseline for VQA(~3.5%), Text Classification(~4%) & Image classification(~2%). Hence, our work got accepted at WACV'2021.

Walmart labs

Summer Intern

May 2018 – Jul 2018 · 2 mos · Bengaluru, Karnataka, India

I worked on integrating the e-commerce platform with the Google Home device. I used Google Dialog Flow to process user speech and recognize the user’s intent and employed Node JS Webhook to connect the Dialog Flow App with Backend APIs.