Lukas Weidenholzer

Resume

Relevant Experience

2023 -
Kaleido AI (part of Canva), Machine Learning Engineer
On the Visual AI Platform team.
2021 - 2023
EODC, Software Developer
Worked with (big) satellite imagery. Wrote & maintained a backend + REST API for on-demand Earth Observation data processing using Python. I also setup and nurtured the underlying Kubernetes cluster and practised CI/CD, GitOps, Devcontainers and Infrastructure-as-Code to stay sane.
2020 - 2021
Research Sabbatical
Took some self-funded time off to figure out what I want to do next. I ended up wildly studying lots of different things in math, history & philosophy that I didn't have time for before. Tech projects I worked on:
  • Built codeatlas.dev, a codebase visualisation tool
  • Created a habit-tracking Slack bot for myself
  • Worked through the fastai course
2018 - 2020
Echobox, Data Scientist
I was responsible for developing, testing, deploying and monitoring machine learning models for a wide variety of use-cases, e.g. scoring, classification and clustering tasks.
2016 - 2017
University College London, Research Assistant
Text-mining a large database of apps to detect free/premium versions of the same app. Work supervised by Dr. Yongdong Liu.

Academia

2021 - 2021
PhD Computer Science, Tufts University
Started a PhD in Computer Science, realized it wasn't for me and moved on.
2017 - 2018
MSc Artificial Intelligence, University of St Andrews
Graduated with Distinction.
Courses on Machine Learning, Search, Logic, Distributed Systems, Software Engineering, Natural Language Processing and Human-Computer Interaction. Master thesis on teaching a virtual robot to juggle.
2014 - 2017
BSc Management Science, University College London
First Class Honours.
Courses on Data Analytics, Algorithms, Machine Learning, Linear Algebra, Calculus, Probability & Statistics, Microeconomics & Game Theory, Finance, Behavioural Science, Design and Decision Theory.

Tech Stack in approximate order of expertise

  • Languages & Frameworks: Python (numpy, pandas, PyTorch, fastai, jupyter, SpaCy, sklearn, dask), Java, Javascript/Typescript (React)
  • Machine Learning: Deep Learning (+ many of its modern flavours), NLP, Reinforcement Learning, Genetic Algorithms, Computer Vision, clustering, dimensionality reduction
  • Tools: git, CI/CD enthusiast, Docker, Kubernetes