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Siddesh Sambasivam Suseela

Hi, I'm Sid 👋

Engineering lead at Hypotenuse AI, building LLM infrastructure for ecommerce product data at scale.

Work

Apr 2026 - Present
Lead Software Engineer · Hypotenuse AI
  • Own the architecture and engineering direction, including the product database, enrichment, and generation systems that handle millions of SKUs every day. Most of my time goes into deciding what to build next and keeping AI and compute costs trending down as we grow.
  • Building the feedback loops and retraining pipelines that turn every correction across Hypotenuse, both explicit edits and implicit signals, into training data for better models over time, along with the internal AI tooling our engineers rely on day to day.
See the full work history

Recent writing

All writing
post Startup Post-Mortem: Lessons from My Startup OdysseyLooking back on a startup that didn't make it — what I learned about engineering, business, design, and the … Jan 29

Selected work

All projects
project MatterIxA tiny deep learning framework, built from scratch to teach the fundamentals. pythondeep-learningautodiff 2020 → 2022 project Learnify AITurn your study notes into an interactive knowledge graph. javascriptknowledge-graphnlpedtech 2020 → 2023 project DetoxA drop-in REST API for multilingual profanity filtering. javamlapihackathon 2021 → 2023 project Newscast APIA simple REST API to fetch news articles for any query. pythonrest-apidatacrawling 2021 → 2022 project ShillingsA mock payments backend for testing client integrations end-to-end. gobackendapisystem-design 2022 project SciML for Knowledge DiscoveryA comparative study of symbolic and sparse regression for scientific ML. researchscientific-mlsymbolic-regression 2022 · ICARCV

Selected publications

Google Scholar
ICARCV 2022 Dec 2022
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A Comparative Study on Machine Learning Algorithms for Knowledge Discovery
A survey and head-to-head benchmark of symbolic regression methods — Genetic Programming, Deep Symbolic Regression, AI-Feynman, and NeSymRes — on the Feynman-03 and Nguyen-12 datasets. We highlight the strengths, failure modes, and open problems for each family of methods.
S. S. Suseela, Y. Feng, K. Mao
Nanyang Technological University 2022
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Scientific Machine Learning for Knowledge Discovery
Bachelor's thesis at Nanyang Technological University. Investigates how machine learning methods can recover interpretable, closed-form models from data — bridging the gap between black-box prediction and scientific insight.
S. S. Suseela