Researcher Profile
Editor reviewedHybrid Transformer–Mamba language models (Jamba)
CTO at AI21 Labs
One of the higher-signal people to know in the hybrid-LLM line because he sits at the point where AI21’s research architecture, long-context systems work, and real product deployment meet.
Organizations
Labs
About This Page
This profile is meant to help you get oriented quickly: why this researcher matters, what to read first, and where to explore next.
Last reviewed
March 18, 2026
Official And External Links
The ideas, systems, and research directions that make this person worth knowing.
01
Technical leadership at AI21 Labs
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Hybrid Transformer-Mamba language models such as Jamba
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Turning model architecture decisions into deployable systems
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Hybrid Transformer–Mamba language models (Jamba)
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Jamba: A Hybrid Transformer-Mamba Language Model
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Jamba-1.5: Hybrid Transformer-Mamba Models at Scale
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Additional papers, projects, or repositories that help flesh out the profile.
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People worth exploring next because they share topics, labs, or source material with this profile.
A valuable page in this cluster because his public role description is unusually specific: post-training, steerability, and AI-generated evaluation data are exactly the kinds of practical problems strong researcher pages should make discoverable.
A distinctive page in this AI21 cluster because she brings a linguistics and human-evaluation angle to model work, especially around user interaction, multilingual language behavior, and how LLM performance gets tested in practice.
Worth knowing because his work links earlier dense-retrieval research to later MRKL and Jamba systems, which makes his page a good bridge between classic NLP retrieval and newer hybrid LLM stacks.
A useful page because it points to the research-and-strategy side of AI21 rather than only the product or engineering side, especially where model evaluation and new architectural bets get shaped at the CTO-office level.
One of the better pages in this cluster because it connects AI21 alignment work to concrete retrieval and grounding research rather than leaving "alignment" as a vague label.
An especially valuable page for understanding how AI systems get judged in practice, because it puts human evaluation and rubric design at the center rather than treating them as an afterthought to model building.