Shahul Shereef
Founder at Vibrant Labs
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolShahul Shereef is a data science and ML engineer with nine years of experience building end-to-end machine learning systems and NLP solutions across startups and product teams. He founded Vibrant Labs to benchmark and improve long-horizon capabilities of AI agents, and previously built production ML pipelines at AMPLYFI and credit-underwriting models using short-text and alternative data. A Kaggle Grandmaster (Kernels) ranked top 20 among 100k+ users, he brings competition-honed modeling rigor to real-world problems and contributes to notable open-source projects like LAION’s Open-Assistant and torch-audiomentations. His open-source work includes converting dialog datasets into instruction-following formats for conversational AI and adding robust audio augmentation and evaluation metrics, reflecting a practical focus on data quality and model evaluation. Based in San Francisco, he combines entrepreneurial drive with hands-on engineering and a knack for turning messy, small-text datasets into deployable ML features.
9 years of coding experience
3 years of employment as a software developer
Bachelor of Technology Computer Science, Bachelor of Technology Computer Science at Govt.model engineering college
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (16)
pytorch10
jupyter-notebook10
machine-learning10
nlp10
data-augmentation10
sentence-transformers10
python10
datasets10
testing10
natural-language-processing9
deep-learning9
language-model8
audio8
ml8
ai7
Programming languages (10)
Github contributions (5)
explodinggradients/ragas
May 2023 - Apr 2025
Supercharge Your LLM Application Evaluations 🚀
Role in this project:
Back-end Developer
Back-end DeveloperContributions:255 reviews, 582 PRs, 319 pushes in 1 year 10 months
Contributions summary:Shahul implemented a BertScore metric and added SBERT score calculation and relative imports within the `belar/metrics/similarity.py` file. Moreover, the user added EditScore metric with distance and ratio measures, and also a Bleu score. They also added Textual Entailment Score, fixed device checks, re-formatted imports and added the Q-square metric.
evaluationllmllmops
asteroid-team/torch-audiomentations
Mar 2022 - May 2022
Fast audio data augmentation in PyTorch. Inspired by audiomentations. Useful for deep learning.
Role in this project:ML Engineer
ML EngineerContributions:5 reviews, 52 commits, 4 PRs in 1 month
Contributions summary:Shahul primarily contributed to implementing and testing audio data augmentation techniques using PyTorch within the `torch-audiomentations` repository. They developed a `RandomCrop` augmentation, including initial implementation, base class initialization, type conversions, and testing. Furthermore, the user added a `Padding` augmentation and contributed to a `SpliceOut` augmentation. The commits focus on enhancing the library's audio processing capabilities, particularly for deep learning applications.
pythondifferentiable-data-augmentationaudio-datadspaudio-effects
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.