Aditya Singh
Research Engineer at Isomorphic Labs
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolAditya Singh is a research-focused machine learning engineer with 8 years of experience building and deploying ML systems across industry and academia, currently at Isomorphic Labs in London. He holds a dual MTech/BTech in Mathematics and Computing from IIT BHU and has contributed at scale to teams at Amazon, Mastercard, Microsoft Research, and IBM Research on topics ranging from recommendation models and graph ML to RL and fairness. His background blends applied production work—quantization, knowledge distillation, VAE/LLM tooling and A/B testing—with deep research into temporal debiasing, evolutionary strategies for game AI, and DRQN architectures. An active open-source contributor, he has optimized classical algorithm implementations and pragmatic back-end code, reflecting both algorithmic rigor and production sensibility. Notably, he pairs strong theoretical foundations with practical wins in deploying ML research into real-world products and experiments.
8 years of coding experience
4 years of employment as a software developer
Kendriya Vidyalaya SangathanIDD - Integrated Dual Degree, Mathematics and Computer Science, IDD - Integrated Dual Degree, Mathematics and Computer Science at Indian Institute of Technology (Banaras Hindu University), Varanasi
Github Skills (21)
algorithm10
algorithms10
c-language10
c1110
data-structure10
c1710
data-structures10
cpp10
cprogramming-language10
math9
mathematics9
computer-science9
javascript4
java4
php4
Programming languages (8)
Github contributions (5)
ZoranPandovski/al-go-rithms
Oct 2017 - Oct 2017
:musical_note: Algorithms written in different programming languages - https://zoranpandovski.github.io/al-go-rithms/
Role in this project:
Back-end Developer
Back-end DeveloperContributions:5 commits, 5 PRs in 2 days
Contributions summary:Aditya primarily focused on enhancing algorithm implementations within the repository. Their contributions included modifying data types for improved accuracy in Fibonacci calculations and optimizing prime number calculations. They also corrected a Fibonacci function and extended the range of factorial calculations. Moreover, the user optimized quadratic equation solutions by removing unnecessary calculations.
pythoncomputer-scienceprogramming-languagejavascriptalgorithms-and-data-structures
adityauser/mlss18-assignment2
Jun 2018 - Oct 2020
ML Summer School (2018) - Assignment 2
Contributions:2 commits, 1 PR, 2 pushes in 2 years 4 months
summer-schoolpythonmachine-learningsummer
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