Isaac Chung
My focus is on making AI systems usable, scalable, and maintainable. I'm currently a Staff Data Scientist at Zendesk QA, working on LLM-powered features that see millions of conversations a day.
Previously at Clarifai, I helped build and maintain multimodal retrieval systems in production. My background is in Aerospace Engineering and Machine Learning and I hold undergraduate (B.A.Sc in EngSci) and graduate (M.A.Sc) degrees from the University of Toronto.
In my spare time, I am a maintainer for MTEB, I like to see the world, and do a bit of swim/bike/run racing.
Zendesk QA
LinkedIn –Session
Reproducibility in embedding benchmarks is challenging, especially with embedding models that are instruction-tuned and increasingly large. Learn how MTEB tackles prompt variability, scaling issues, and large datasets to ensure fair and consistent evaluations, setting a standard for benchmarking in embeddings.