Job Description

Tobi Olatunji is a physician turned Machine Learning Scientist with a passion for Global Health. 

Tobi’s journey to starting Intron is nothing short of epic. He’s worked with big names like AWS Health AI, Enlitic Inc., and Cambia Health Solutions building intelligent Natural Language processing tools for healthcare used in Australia, Brazil, Canada, Japan, UK, and the US. Now, he’s on the Advisory Board at Harvard’s OpenNotes Lab, pushing for responsible AI in healthcare. His research spans accented speech recognition, algorithmic bias, and more, with publications in top machine learning conferences like NeurIPS, Interspeech, and EMNLP.

With a mix of medical and tech degrees, including an MBBS, an MSc in Medical Informatics, and MSc in Computer Science at Georgia Tech, a certificate in Healthcare Management from Yale School of Management, plus three US tech patents, Tobi is a true innovator. He’s also a key member of the Commonwealth AI Consortium and the Research Director at Bio-RAMP Labs.

The Basics

Product demo: https://youtu.be/ZV10YNUNGYY

Audio Intelligence for Health: https://youtu.be/mTNWytb3x2I

Website: https://intron.io

Open positions: https://intron.io/about/#jobs

Recent Press

• TechCrunch pre-seed fundraise announcement
• Google Research, Gates Foundation, and Intron collaboration: link
• NVIDIA AI Blog: https://blogs.nvidia.com/blog/2023/05/11/ai-africa-doctors-paperwork/
• BBC News Interview: https://www.bbc.com/news/av/world-africa-66033811
• Commonwealth Partnership Announcement: link and link
• Harvard+BIDMC OpenNotes Advisory Board: link
• Harvard Radcliffe Consortium on scaling Digital Health Innovations in Africa: link
• TechCabal article: https://techcabal.com/2023/05/29/intron-health-brings-ai-to-african-healthcare/
• NVIDIA and Huggingface collaborate with Intron for Zindi Challenge/Hackathon: https://zindi.africa/competitions/intron-afrispeech-200-automatic-speech-recognition-challenge

Research Papers

Cool research papers coming out of our lab:

1. AfriSpeech [MITPress 2023]: our pan-African clinical speech dataset with benchmarks
2. AfriNames [Interspeech 2023]: most ASR models butcher African names
3. AccentFolds [EACL 2024]: models can learn African cultural and geopolitical relationships from speech data
4. ASR on Medical Entities [Interspeech 2024]: models can work great on non-medical speech but perform worse on medical terms
5. AfroTTS [Interspeech 2024]: A 1000 African Voices; generating English speech in nearly 1000 African accents
6. AfriMed-QA [ACL 2025]: the largest study on LLMs in African healthcare

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