Páidí Creed
Vice President Of Engineering at Relation
England, United Kingdom
Summary
👤
Senior🎓
Top SchoolPáidí Creed is a VP of Engineering and computational biology technology leader with 11+ years building production-grade AI, bioinformatics platforms, and ML-driven drug discovery tools. He has a strong track record scaling teams from small R&D groups into multifunctional engineering departments, most recently growing biomodal’s computational organisation to 20+ developers and supporting a Series D raise. Equally comfortable in research and product environments, he moved teams at BenevolentAI from R&D into cross-functional product delivery and has deep experience translating multi-omics and novel 5-/6-letter genome data into deployable pipelines. Technically grounded in algorithms and systems from a PhD in Informatics, he also contributes to open-source ML tooling (e.g., improvements to Snorkel tutorials and Spark RDD optimisations), reflecting a hands-on approach to data processing at scale. Colleagues describe him as iterative, data-driven, and focused on transparent, empathetic engineering cultures that bridge science and commercial impact.
11 years of coding experience
13 years of employment as a software developer
PhD Informatics, PhD Informatics at The University of EdinburghBSc Computer Science, BSc Computer Science at University College Cork
Stackoverflow
145reputation
3kreached
0answers
3questions
Github Skills (12)
machine-learning10
python10
snorkel10
data-science9
ai9
spark9
user-manual8
data-augmentation8
basics8
learn-ruby-on-rails8
python-modules6
googletest6
Programming languages (6)
Github contributions (5)
snorkel-team/snorkel
Jul 2017 - Aug 2017
A system for quickly generating training data with weak supervision
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 1 PR, 3 comments in 26 days
Contributions summary:Páidí primarily contributed to the Snark tutorial, which focuses on a weak supervision approach for generating training data. Their work involved updating the Snark tutorial to utilize the latest Sentence schema, including absolute character offsets, which suggests involvement in the core data handling or model training pipeline. They also implemented filtering of the RDD when generating the split cache, demonstrating an understanding of data processing optimization within a Spark environment and added a timer to the label annotator.
weak-supervisionpythondata-sciencemachine-learninglabeling
paidi/emacs.d
Apr 2015 - Nov 2019
Contributions:15 pushes, 1 branch in 4 years 8 months
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