Daniel Saunders
Senior Machine Learning Engineer at Spotify
Massachusetts, United States
Summary
🤩
Rockstar🎓
Top SchoolDaniel Saunders is a Senior Machine Learning Engineer with 11 years of experience applying ML and AI research to production personalization systems, currently at Spotify. He combines a PhD-level scientific grounding in psychology and cognition with hands-on experience shipping models from prototype to customer-facing deployments in modern production environments. His background spans personalization, pricing algorithms, and tooling for LLMs, RL, and computer vision, and he contributes to open-source projects including spiking neural network simulations (BindsNET) and LLM tooling (Axolotl). Notably, he began his career doing eyetracking and fMRI research and holds a patent for a text-scoring algorithm, reflecting a rare blend of behavioral science and practical ML engineering. Colocated in Massachusetts, he focuses on translating rigorous data-driven insight into scalable, user-centered machine learning products.
11 years of coding experience
15 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Psychology, Doctor of Philosophy (Ph.D.), Psychology at Queen's UniversityBachelor of Math, Computer Science, Bachelor of Math, Computer Science at University of Waterloo
Github Skills (16)
simulations10
simulation10
command-line-interface10
machine-learning10
pytorch10
python-lib10
python-click10
neurons10
pytest10
python10
cli10
testing10
configuration-management9
automations8
automation8
Programming languages (6)
Github contributions (5)
BindsNET/bindsnet
Jul 2018 - Sep 2020
Simulation of spiking neural networks (SNNs) using PyTorch.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:3 releases, 119 commits, 127 PRs in 2 years 1 month
Contributions summary:Daniel's commits primarily involve modifications to the Python code within the `bindsnet` repository, focused on improving the performance and functionality of the spiking neural network (SNN) simulations. They appear to be involved in fixing the existing code by debugging, cleaning up the code, and updating simulation models, and introducing new features. Key areas of modification include the core nodes (neurons) and the supporting topology and dataset modules to match existing codebase.
pytorchsimulationspiking-neural-networksgpu-computingmachine-learning
axolotl-ai-cloud/axolotl
Nov 2024 - Oct 2025
Go ahead and axolotl questions
Role in this project:Full-stack Developer
Full-stack DeveloperContributions:267 reviews, 109 PRs, 665 pushes in 10 months
Contributions summary:Daniel primarily contributed to the implementation of a CLI (Command Line Interface) for the axolotl project, including initial setup using the `click` package, addition of fetch command and test integration. They focused on argument parsing, configuration, and testing, demonstrating strong involvement in defining and testing the command-line interface. The user also addressed build and test related issues.
gofine-tuningllm
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