Joel Kronander
Principal TLM at Aurora
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolJoel Kronander is a Principal TLM based in Palo Alto with a decade of experience building production-grade machine learning and computer vision systems across startups and advanced research labs. He progressed from PhD research and co-founding a startup to leadership roles at Apple, Nines (Forbes AI50), Scale AI, and Aurora, where he now leads technical lifecycle management at a senior level. Joel combines hands-on ML engineering—contributing to open-source projects like TensorLayer—with product and team leadership, having headed synthetic data and ML teams that bridge research and operations. He holds a PhD from Linköping University and an academic background spanning EPFL exchange work, which underpins his strength in turning complex algorithms into deployable systems. An uncommon thread in his career is frequent role-switching between deep research and practical engineering, giving him a rare fluency in both prototyping novel models and scaling them for real-world autonomy platforms.
10 years of coding experience
15 years of employment as a software developer
ERASMUS Exchange Mathematics and Computer Science, ERASMUS Exchange Mathematics and Computer Science at EPFLDoctor of Philosophy (PhD), Doctor of Philosophy (PhD) at Linköping University
Swedish, math, English
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
machine-learning10
deep-learning10
tensorflow10
python10
neural-network9
deconvolution9
tensorboard9
image-processing8
reinforcement-learning7
convolutional-neural-networks7
Programming languages (5)
Github contributions (5)
tensorlayer/TensorLayer
Jan 2017 - Jan 2017
Deep Learning and Reinforcement Learning Library for Scientists and Engineers
Role in this project:
ML Engineer
ML EngineerContributions:6 commits, 6 PRs, 12 comments in 20 days
Contributions summary:Joel contributed to the TensorLayer library by implementing and refining features related to deep learning and TensorFlow. Specifically, the user added a bilinear upsampling initializer for deconvolutional layers. They also fixed compatibility issues with `tf.image.per_image_whitening` and made minor adjustments to the example code. Furthermore, they added a feature for tensorboard visualization of network parameters during the fitting process.
tensorflow-tutorialscientistspythongoogledeep-reinforcement-learning
kafischer/terminal-copilot
Dec 2022 - Jan 2023
A smart terminal assistant that helps you find the right command.
Contributions:5 releases, 3 reviews, 54 commits in 1 month
chat-gptassistantcopilotgpt3openai
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