Iver Jordal
Research Engineering at ElevenLabs
Trondheim, Trøndelag, Norway
Summary
🤩
Rockstar🎓
Top SchoolIver Jordal is a research engineer and machine learning specialist with 13+ years of software experience, focused on audio and music tech since 2015 and now working at ElevenLabs. Creator and long-term maintainer of the widely used audiomentations library, he combines deep learning research (PyTorch) with pragmatic engineering—optimizing pipelines in C/Rust/Go, building dataset tooling, and deploying production systems. He thrives in deep-tech startups, blending generalist infrastructure and backend skills with specialist audio model architecture and multichannel processing expertise. An open-source enthusiast who writes Python daily, he also brings practical embedded and performance tuning experience (SIMD, multithreading) uncommon for research engineers. Outside work he’s a demoscener and Meteoriks award winner, vocalist, dad and mountain biker—skills that hint at a creative, hands-on approach to hard problems.
13 years of coding experience
14 years of employment as a software developer
Helårskurs Musikk, Helårskurs Musikk at Voss FolkehøgskuleNorwegian University of Science and Technology
Norwegian, English, Dutch
Github Skills (17)
pytorch10
python10
machine-learning10
audio-processing10
numpy10
convolution10
librosa10
data-augmentation10
unit-test10
ffmpeg9
operation9
scipy9
fftw9
tensorrt9
tensorflow9
Programming languages (14)
Github contributions (5)
asteroid-team/torch-audiomentations
Sep 2020 - Jan 2023
Fast audio data augmentation in PyTorch. Inspired by audiomentations. Useful for deep learning.
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:15 releases, 97 reviews, 327 commits in 2 years 4 months
Contributions summary:Iver primarily contributed to the development and testing of audio data augmentation functionalities within the PyTorch framework. They added test fixtures to facilitate testing and implemented a PyTorch version of the `convolve` function, including unit tests. Further contributions involved the creation and testing of a `PolarityInversion` transformation, as well as setting up the project's setup.py file and demonstrating usage with a demo script.
pythondifferentiable-data-augmentationaudio-datadspaudio-effects
iver56/audiomentations
Feb 2019 - Jan 2023
A Python library for audio data augmentation. Inspired by albumentations. Useful for machine learning.
Role in this project:ML Engineer
ML EngineerContributions:33 releases, 129 reviews, 676 commits in 4 years
Contributions summary:Iver significantly contributed to the development of an audio data augmentation library. Their work included implementing new audio transformation techniques such as TimeStretch, PitchShift, and a limiter, along with associated unit tests to ensure quality. The user also enhanced the library by introducing new features for multichannel audio processing.
pythonalbumentationsaudio-datadspaudio-effects
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.