Headline

Applied ML Research | Recommendation Systems • LLMs | Research Scientist Intern @ Rakuten

About

I love working on problems where machine learning can genuinely improve user experiences. Currently exploring recommendation systems at Rakuten and finding them more interesting than I expected. I'm particularly interested in recommendation engines, LLMs, and applied ML research, and I enjoy exploring how ideas from research can be turned into practical systems. Always happy to connect with fellow builders and researchers.

Location

Bengaluru

AI stack

claude code

codex

cursor

hugging face

notebooklm

obsidian

Work experience

Research scientist intern at Rakuten, May 2026 - Present · 5 mos, Bengaluru, Building user representation learning systems for recommendation and advertising using 600M+ behavioral events from 25M+ users. Developed 256D user embeddings with contrastive learning, improving Recall@10 from 0.397 to 0.768 for next-interaction prediction. Applied learned embeddings to ad targeting, improving offline ROC-AUC from 63.69 to 67.89 in a LightGBM model.

ML Engineer Intern at Peakflo (YC W22), Apr 2026 - May 2026 · 2 mos, Remote

GenAI Intern at EXL, Jul 2025 - Sep 2025 · 3 mos, Noida, Uttar Pradesh, India

Junior Council Member at NSCC BPIT, Oct 2023 - Mar 2024 · 6 mos, Delhi, India

UX Researcher at Brihat Infotech, Sep 2023 - Oct 2023 · 2 mos, Remote

Tech Team Member at NSCC BPIT, Mar 2023 - Mar 2025 · 2 yrs 1 mo

Education

Bachelor of Technology - BTech, Guru Gobind Singh Indraprastha University

12th, Little Flowers Public Sr Sec School

Public links

github: https://github.com/nkhanna94

leetcode: https://leetcode.com/niharikakhanna