Emmanuel Ameisen
Emmanuel Ameisen is an interpretability researcher at Anthropic whose work studies how language models operate internally. He built machine-learning systems for Local Motion and Zipcar, led Insight Data Science's AI program, and holds graduate degrees in artificial intelligence, computer engineering, and management. He published a guide to building machine-learning applications in 2020 and worked on Claude fine-tuning and interpretability at Anthropic by 2024. His later work examines hidden model objectives, traces circuits inside language models, and studies how models detect changes to their internal representations.
Timeline
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2018–2020
Insight's AI program
In October 2018, he leads Insight Data Science's AI program and develops an image-search system using convolutional neural networks and word embeddings. Before Insight, he builds and deploys predictive-analytics and machine-learning systems for Local Motion and Zipcar; by 2020, he holds graduate degrees in artificial intelligence, computer engineering, and management from three French schools.
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January 2020
A book on applied ML
He publishes Building Machine Learning Powered Applications: Going from Idea to Product in January 2020. The book guides readers through designing, building, and deploying a machine-learning application from its initial idea.
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August 2024
Research Engineer at Anthropic
In August 2024, he is a Research Engineer at Anthropic, where he fine-tunes Claude and helps with interpretability research.
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March 2025
Auditing hidden model objectives
He coauthors Auditing language models for hidden objectives in March 2025. The study trains a model with a hidden objective, runs a blind auditing game with four research teams, and examines eight auditing techniques.
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May 2025
Open-sourcing circuit-tracing tools
In May 2025, Anthropic releases a circuit-finding library developed by fellows Michael Hanna and Mateusz Piotrowski with mentorship from Emmanuel Ameisen and Jack Lindsey. The tools let researchers trace model circuits, visualize graphs, and test hypotheses by changing feature values.
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2026
Mechanisms of introspective awareness
In 2026, he coauthors Mechanisms of Introspective Awareness, which studies how language models detect injected steering vectors and identify the injected concept. The paper finds that distributed internal computations support this detection.
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As of October 2026
Interpretability research at Anthropic
He is an interpretability researcher at Anthropic, where he focuses on understanding how large language models work internally.
Sources and credits
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- Emmanuel Ameisen, Head of AI at Insight, on Building a Semantic Search System for Images
- Emmanuel Ameisen — AI Council
- Building Machine Learning Powered Applications: Going from Idea to Product - Emmanuel Ameisen - Google Books
- Integrating LLMs Into Products - Video | Agentic AI Foundation
- Auditing language models for hidden objectives
- Open-sourcing circuit-tracing tools
- Mechanisms of Introspective Awareness
- The AI Conference 2026 Speakers: Full Lineup | Mezzo

