Esben Sørig
Co-Founder, CTO at Kale AI
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolEsben Sørig is a computer scientist and startup CTO with 12 years of experience blending software engineering, computational statistics, and interactive machine learning. As Co-Founder and CTO of Kale AI he builds technology aimed at decarbonising cities, translating academic research into product-grade systems. His background includes data science roles at swhere, a research internship at Microsoft Research, and teaching interactive ML as an associate lecturer at Goldsmiths, reflecting a rare mix of applied research and production delivery. Early engineering work at Trifork and open-source contributions—such as implementing a meta-language parser and translator for the Shōgun ML toolbox—underscore his strength in tooling and language-oriented back-end systems. He holds a BSc in Computer Science and an MSc in Computational Statistics & Machine Learning from UCL and is pursuing a PhD in Interactive Machine Learning, bringing rigorous academic depth to practical climate-tech challenges. Colleagues describe him as someone who moves smoothly between prototype research and scalable implementation, often spotting opportunities to automate repetitive ML workflows.
12 years of coding experience
6 years of employment as a software developer
PhD candidate, Interactive machine learning, PhD candidate, Interactive machine learning at Goldsmiths, University of LondonUniversity College London
Github Skills (11)
parser10
parsing10
python10
parse10
cprogramming-language9
machine-learning9
c-language9
json9
ply9
octave7
cmake5
Programming languages (6)
Github contributions (5)
shogun-toolbox/shogun
Nov 2014 - Jan 2019
Shōgun
Role in this project:
Back-end Developer
Back-end DeveloperContributions:43 commits, 38 PRs, 120 comments in 4 years 2 months
Contributions summary:Esben implemented the initial version of a meta-language parser for the Shōgun machine learning toolbox. They defined a grammar for parsing a minimal example and generated an Abstract Syntax Tree (AST) in JSON format. The user also added a translation program to translate the parsed AST to target languages. The initial implementation included support for Python and Octave targets and integrated unit tests for the Python target. The user extended the grammar and made interfaces of the parsing and translation programs consistent.
cmakedata-sciencegunc-plus-plusmachine-learning
sorig/moodle-scraper
Mar 2014 - Feb 2016
Contributions:13 commits, 1 push in 1 year 11 months
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