---
title: "Akarsh Upadhyay - Applied Scientist 2 in Bengaluru | Tal"
description: "Meet Akarsh Upadhyay, Applied Scientist 2 @ Microsoft | Recommendation & NLP | Focused on foundational ML work based in Bengaluru. AI stack includes hugging face, mcp, and huggingface. Explore public work links including github, youtube.com, and uakarsh.github.io."
image: "https://tal.club/meet/akarsh-upadhyay/share-image"
---

# Akarsh Upadhyay on Tal

## Headline

Applied Scientist 2 @ Microsoft \| Recommendation & NLP \| Focused on foundational ML work

## About

I build ML systems where the solution becomes the foundation for what comes next\. Right now at Microsoft, I work on Ads Trust & Safety — owning the end\-to\-end ML for a core ad policy: detection, measurement, and the infrastructure that determines whether enforcement is actually working across Search and Native platforms\. Before moving into ATS, I spent a year and a half building retrieval models for ad selection: intent\-based encoder models, evaluation frameworks, and the data pipelines behind it all\. Before Microsoft, I shipped AI systems to millions of users at Zomato everything from image quality scoring, Personalized Photo Cakes to PoC for automated ad creation\. At Enterpret, I researched text similarity at scale, spending most of my time on data preparation because that's usually where projects succeed or fail\. What drives me: I care about foundational work\. The kind where if you get it right, it unlocks progress for everyone who comes after, be it data, modeling or evaluations\!

## Location

Bengaluru

## AI stack

hugging face

mcp

huggingface

ChatGPT

## Work experience

Applied Scientist 2 at Microsoft AI, Mar 2026 \- Present · 7 mos, Bengaluru

Applied Scientist at Microsoft AI, Sep 2024 \- Feb 2026 · 1 yr 6 mos, On\-site, \- Developing next\-gen retrieval models for ad selection across Microsoft Audience Network \- Built unified evaluation framework adopted as standard across regional ML teams \- Established foundational data quality practices now used across MSAN globally \- Driving production impact on ad monetization through principled ML approaches

Machine Learning Engineer at Zomato, Jan 2024 \- Aug 2024 · 8 mos, Gurugram, \- Built image quality system evaluating every food image shown to millions of users \- Led the PoC for automated ad creation using generative models for brand\-specific content \- Launched photo cake feature—personalized orders at scale across India

Machine Learning at Enterpret, Jun 2023 \- Dec 2023 · 7 mos, On\-site, \- Researched text similarity methods for business intelligence at million\-text scale \- Created data frameworks and supervised annotation teams for quality training data \- Combined prompt engineering, fine\-tuning, and novel loss functions for strong results

Machine Learning Intern at Department of Computer Science and Engineering, IIT Jodhpur, Jul 2021 \- May 2022 · 11 mos, India, Guide: Dr\.Santanu Chaudhary \- My main work includes searching and developing ML models for the purpose of Document Layout Understanding \- To be more specific, the ML models are inspired by the transformers network, and there are some novel losses that have been incorporated for the same\.

## Education

Bachelor of Technology \- B Tech, Electrical Engineering, Indian Institute of Technology Jodhpur

## Public links

- github: https://github\.com/uakarsh
- youtube\.com: https://youtube\.com/@akarshupadhyay1241
- uakarsh\.github\.io: https://uakarsh\.github\.io/index\.html

[View the original profile](https://tal.club/meet/akarsh-upadhyay)

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