Darek Kłeczek
Senior Research Scientist at Snowflake
Warsaw, Masovian Voivodeship, Poland
Summary
🤩
Rockstar🎓
Top SchoolDarek Kłeczek is a Senior Research Scientist at Snowflake with a decade of experience applying machine learning across industry and research, from P&G’s supply chain and manufacturing to developer-focused roles at Weights & Biases. He built and taught LLM applications and practical ML courses, and trained the first Polish BERT and GPT models—demonstrating both production savvy and language-specific research depth. A four-time Kaggle Grandmaster (top 27 globally in competitions), he also contributes to high-profile open-source projects like the Llama Cookbook, where he integrated Weights & Biases logging for improved experiment tracking. Comfortable bridging enterprise delivery and cutting-edge research, Darek combines hands-on engineering, reproducible ML practices, and a track record of translating models into business impact. Based in Warsaw, he balances technical rigor with mentorship and a curiosity for practical tooling that accelerates ML adoption.
10 years of coding experience
19 years of employment as a software developer
Master’s Degree Information Systems, Master’s Degree Information Systems at Johannes Gutenberg University MainzMaster’s Degree Information Systems Management, Master’s Degree Information Systems Management at SGH Warsaw School of Economics
English, Spanish, German, Polish
Github Skills (16)
scikit10
data-preprocessing10
llm10
splitting10
pandas10
llama10
pytorch10
machine-learning10
python10
wandb10
splits10
fine-tuning10
scikit-learn10
image-processing9
deep-learning9
Programming languages (3)
Github contributions (5)
wandb/edu
Jun 2022 - Jan 2023
Educational materials on deep learning by Weights & Biases
Role in this project:
Data Scientist
Data ScientistContributions:8 reviews, 47 commits, 62 PRs in 7 months
Contributions summary:Darek's primary contribution involves the preparation and preprocessing of a dataset for a deep learning project focused on image classification, likely for detecting mold in fruit. They developed data splitting strategies into train, valid, and test sets using stratified group k-fold cross-validation. The user implemented data preparation steps including artifact creation, annotation loading, and merging dataframes to facilitate model training and evaluation.
fairness-mldeep-learningbiasesweightsmachine-learning
meta-llama/llama-cookbook
Nov 2023 - Nov 2023
Welcome to the Llama Cookbook! This is your go to guide for Building with Llama: Getting started with Inference, Fine-Tuning, RAG. We also show you how to solve end to end problems using Llama model family and using them on various provider services
Role in this project:ML Engineer
ML EngineerContributions:5 reviews, 5 comments in 22 days
Contributions summary:Darek implemented and integrated Weights & Biases (wandb) logging into the fine-tuning process for the Llama model. This involved modifications to the `finetuning.py` and `train_utils.py` files to track training metrics, including loss and perplexity. The user also configured wandb settings and integrated logging within the evaluation loop, enhancing the experiment tracking capabilities of the Llama Cookbook.
aifinetuninglangchainllamallama2
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