Jeroen Van Goey
Staff Research Engineer in BioAI building generative models for biology
Machine learning research engineer, fascinated by the intersection of AI and science. I bridge scientific ML, biological domain knowledge, production-quality software, and team leadership.
Currently looking for Staff/Principal Research Engineer roles in AI for science (biology, proteomics, drug discovery, protein design, foundation models).
- Published InstaNovo (a transformer) and InstaNovo+ (a diffusion model) for de novo peptide sequencing in Nature Machine Intelligence.
- Led BioAI teams working on peptide sequencing and signal-peptide design.
- Built and shipped production-grade ML systems across GPUs, HPC, cloud, and scientific data pipelines.
What I work on
I build and ship machine-learning systems for science: generative sequence models, calibrated and reliable predictions, and the large-scale data and training behind them. My main application is de novo proteomics: reading protein and peptide sequences directly from raw mass spectra.
- Generative sequence models: transformer and diffusion models that decode sequences from noisy scientific signals (InstaNovo / InstaNovo+), top-ranked across an independent benchmark of 17 tools on 83 datasets.
- Reliable, calibrated predictions: rescoring, calibration and false-discovery-rate control so model outputs carry statistical guarantees (Winnow).
- Large-scale data & training: curating large public datasets with automated LLM labelling, and distributed training across GPUs, HPC and cloud.
- Research to production: turning models into reusable, production-grade libraries and an end-to-end pipeline (prediction, scoring, quantification, structural assembly).
Publications
InstaNovo in the news
The InstaNovo paper drew broad attention across the scientific press.
- ScienceAn AI revolution comes to protein sequencing
- Science NewsAI may help decode proteins DNA can't reveal
- Chemistry WorldAI takes step towards cracking biology's toughest problem
- BioTechniquesDoubling up: novel AI models improve de novo peptide sequencing
- Technology NetworksAI models accelerate discovery within protein science
- AZoLifeSciencesNovel AI models enhance disease diagnosis and pathogen ID
- ScienceDailyPossible game-changers within protein science and healthcare
- Mirage NewsAI models revolutionize protein science
- The Research CodeAI system reads protein sequences without databases
- Dong-a ScienceAI cracks protein sequencing (Korean)
More coverage: press releases, blogs & social
Institutional press releases: InstaDeep · DTU · Science News Denmark (Novo Nordisk Foundation)
Blogs & newsletters: Plenty of Room · Proteomics News
Community: r/proteomics · r/massspectrometry
On X: InstaDeep · viral thread (149.8K views) · Chemistry World · A. Laustsen · T. P. Jenkins · J. Bravo-Abad
On LinkedIn: InstaNovo · Proteomics & Immunopeptidomics · M. Busch · A. Laustsen
After publication we released a next generation of InstaNovo models: Introducing the next generation of InstaNovo models (announcement on X · on LinkedIn).
More at the InstaNovo project website and its blog.
Projects
A comprehensive, interactive map of the field: algorithms, post-processors, downstream applications, and adjacent tools, deep-learning and classical alike.
Experience
InstaDeep
Staff Research Engineer in BioAI
July 2024 – Present
Cape Town, South Africa
Team lead and hiring manager for two cross-disciplinary BioAI research teams: de novo peptide sequencing (in collaboration with the Technical University of Denmark) and signal-peptide design for secretion efficiency in immunotherapy and vaccines (with BioNTech). Responsible for recruitment and team expansion across the Cape Town and Kigali offices.
Senior BioAI Software Engineer
August 2022 – July 2024
Cape Town, South Africa
Designed, implemented and delivered performant, scalable protein-design libraries (Transformer-based, written in Python / PyTorch) running compute-intensive workloads on large, complex data across GPUs, HPC supercomputers and the cloud.
Barco
Sr. Software Development Engineer, Machine Learning
February 2020 – August 2022
Kortrijk, Belgium
Built a TensorFlow Extended production pipeline training deep-learning models on multispectral images to detect and classify melanoma skin cancers, working across the research, cloud-backend and mobile/web frontend teams.
BASF
Bioinformatics Researcher, Manager of the Python & R Platforms
August 2018 – January 2020
Zwijnaarde, Belgium
Owned the Python/R data-analysis platform used by 480 researchers and data scientists: technical support, proactive monitoring and root-cause analysis, training (NumPy, pandas, BioPython, …) and mentoring across the scientific community.
Bayer Crop Science
Bioinformatics Researcher & Python Platform Manager
February 2018 – July 2018
Zwijnaarde, Belgium
Same platform role, prior to acquisition by BASF Agricultural Solutions.
Applied Maths, a bioMérieux company
Bioinformatics Software Developer
September 2011 – January 2018
Sint-Martens-Latem, Belgium
Worked on BioNumerics, a bioinformatics suite for integrated analysis of biological data, writing custom Python scripts and extensions for clients in the clinical, academic and government sectors.
Education
Hogeschool West-Vlaanderen (Howest)
2018–2019
Microdegree, Machine Learning & Deep Learning
Katholieke Universiteit Leuven
2010–2012
Bioinformatics, partial credits obtained (M.Sc. not completed)
University of Antwerp
1997–2004
M.Sc., Biology
Hackathons
I have co-created, organized and judged machine-learning hackathons at the Deep Learning Indaba and its regional IndabaX events.
Training a Machine Learning Interatomic Potential (MLIP) model (predicting the energy and forces of atomic systems) with the mlip library, using the 3BPA molecule (3-(benzyloxy)pyridin-2-amine) as the benchmark system.
Using InstaNovo and de novo peptide sequencing to characterise African snake venoms for better antivenoms.
Predicting desert locust breeding grounds from remote-sensing data, with teams competing on the Zindi leaderboard.
Skills
Machine learning
Transformers, diffusion models, large language models (LLMs), supervised & self-supervised learning
Frameworks
PyTorch, TensorFlow / TFX, NumPy, pandas
Scale & MLOps
Distributed GPU training, HPC clusters (on-premise and the MareNostrum 5 supercomputer), cloud (AWS), Docker, CI/CD
Bioinformatics
BioPython, BLAST, ClustalW, Snakemake, BioNumerics
Languages
Python (primary), R
Just for fun
My solutions to Project Euler, a series of challenging mathematical and computer-programming problems.
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My solutions to Advent of Code, the annual December programming-puzzle event (156 stars across 2015–2024).
Just another genome hacker. 🧬