Maciej Rapacz
Senior Software Engineer at Schibsted
Krakow, Lesser Poland Voivodeship
Summary
👤
Senior🎓
Top SchoolMaciej Rapacz is a Senior Software Engineer in Krakow with 10 years of experience building data-heavy, production-grade systems for ad tech and analytics. He specializes in distributed ETL and targeting pipelines running on Kubernetes, Spark, Airflow and a mix of cloud data stores (DynamoDB, Iceberg, Snowflake, S3, Elasticsearch). His background spans machine learning and NLP—he contributed to Stanford's Stanza dependency parser and built real-time parsing services—bringing a research-informed approach to engineering. At Tessian he architected a scalable load-testing framework that replayed traffic from 100k inboxes, and at Yelp he delivered automated reporting pipelines for multi-location businesses. Currently focused on ad insights and predictive/contextual targeting at Schibsted, he combines rigorous data engineering with practical ML experience and a PhD-level academic foundation.
10 years of coding experience
4 years of employment as a software developer
High School Algorithms, High School Algorithms at V Liceum Ogólnokształcące im. Augusta Witkowskiego w KrakowieDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at AGH University of Krakow
Polish, English, French, Swedish, Arabic, Greek
Stackoverflow
959reputation
197kreached
11answers
7questions
multiprocessing
top-5%
Github Skills (18)
artificial-intelligence10
python10
testing10
machine-learning10
natural-language-processing10
nlp10
pytorch9
deeplearning-ai9
deep-learning9
multiprocessing9
elasticsearch6
pycharm6
anaconda6
elasticsearch-dsl6
opencv6
Programming languages (9)
Github contributions (5)
stanfordnlp/stanza
Oct 2019 - Oct 2019
Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 3 PRs, 4 comments in 27 days
Contributions summary:Maciej primarily contributed to the development and testing of the Stanford NLP library, specifically focusing on the dependency parsing module. Their work involved implementing features to handle pre-analyzed input, writing tests to ensure the correctness of these changes, and updating the code to match the development branch. This included modifying core files related to the dependency parser and the pipeline to accommodate the new functionality.
nlppytorchhuman-languagesnamed-entity-recognitionpython
mrapacz/nlp
Mar 2018 - Oct 2019
Assignments from AGH UST Natural Language Processing course
Contributions:8 PRs, 66 pushes in 1 year 7 months
nlpaghlanguage-processingnatural-languageust
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.