Peter Sobot
Staff Machine Learning Engineer at Spotify
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolPeter Sobot is a Staff Machine Learning Engineer with 15 years of experience building high-performance, production ML systems for music at Spotify and incident-critical infrastructure at PagerDuty. He led Music Intelligence efforts that accelerated audio research 40x, trained and deployed large audio models across cloud and mobile, and stepped into interim management for engineering teams. A pragmatic backend engineer, he rewrote core recommendation engines in C for 3x performance and contributes to widely used open-source projects like Spotify’s Annoy (adding a dot-product metric) and TensorFlow Datasets (multichannel audio). Equally comfortable in low-level C++ and high-level ML tooling, he pairs rigorous testing and automation with product-minded releases. Based in New York, he blends a musician’s sensibility with engineering craft—he literally teaches computers to listen to music and has shipped audio features you’ve likely heard as ringtones.
15 years of coding experience
10 years of employment as a software developer
Bachelor's Degree, Honours Software Engineering, Bachelor's Degree, Honours Software Engineering at University of Waterloo
English, French
Stackoverflow
2,551reputation
127kreached
60answers
3questions
ajax
top-5%
javascript
top-5%
partial-views
top-5%
Github Skills (47)
algorithm10
unit-testing10
algorithms10
c-language10
dataflow-programming10
approximate10
dataflow10
python10
nearest-neighbors10
testing10
machine-learning10
approximate-nearest-neighbor-search10
audio-processing10
data-structure10
numpy10
Programming languages (12)
Github contributions (5)
spotify/pedalboard
Jul 2021 - Jan 2023
🎛 🔊 A Python library for audio.
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:111 releases, 67 reviews, 450 commits in 1 year 6 months
Contributions summary:Peter primarily contributed to the development of the `pedalboard` library by adding and improving audio effects plugins. Their work included implementing a distortion effect and associated tests, as well as refactoring the core processing logic to support audio processing state between calls. They also added unit tests to improve the robustness of the codebase.
python-libraryaudio-processingpythonaudiovst3-host
spotify/scio
Apr 2018 - Mar 2021
A Scala API for Apache Beam and Google Cloud Dataflow.
Role in this project:Back-end Developer
Back-end DeveloperContributions:2 reviews, 18 commits, 18 PRs in 3 years
Contributions summary:Peter primarily contributed to the `scio` library, a Scala API for Apache Beam and Google Cloud Dataflow. Their work focused on enhancing the library's functionality, specifically by adding support for empty lists in `unionAll` operations and introducing `BinaryIO` for raw byte file output. They also addressed bug fixes, including a fix for sharded Sparkey side input with missing values, and improved the testing framework to catch duplicate input/output usages.
apidatabeambatchbigquery
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