Catherine Olsson
Catherine Olsson is an AI researcher whose career spans machine-learning engineering, alignment and interpretability. She graduated from MIT in 2012, studied computational neuroscience at NYU, and moved from OpenAI to Google Brain. At Open Philanthropy she funded AI research, then became a research engineer at Anthropic and coauthored papers on helpful assistants and induction heads. As of October 2026, she works in program management at Anthropic.
Timeline
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2012–2018
From MIT to Google Brain
She graduates from MIT in 2012 after studying computer and cognitive science, then studies at NYU's Center for Neural Science. In 2018, she works as a research software engineer at Google Brain after working as a software engineer at OpenAI.
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2020
Grantmaking for AI research
At Open Philanthropy, she serves as a grantmaker and funds technical research that could help prepare for possible AI futures.
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2021–2022
Assistant alignment papers
She coauthors A General Language Assistant as a Laboratory for Alignment (2021), Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback (2022), and Constitutional AI: Harmlessness from AI Feedback (2022). These papers test language-model training methods that use human preferences and AI feedback to shape assistant behavior.
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2022
A paper on induction heads
She works as a research engineer at Anthropic and is the lead author of In-context Learning and Induction Heads, a 2022 mechanistic interpretability paper.
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2023
A study of human V1–V3
She coauthors a 2023 study of orientation-tuned surround normalization in human V1–V3.
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As of October 2026
Program management at Anthropic
As of October 2026, she works in program management at Anthropic.
Career
- Since 2022AnthropicResearch Engineer
- From 2020Open PhilanthropyGrantmaker
- Until 2018OpenAISoftware Engineer
- From 2018Google BrainResearch Software Engineer
Education
Most-cited works
- Dota 2 with Large Scale Deep Reinforcement Learning
- Training a Helpful and Harmless Assistant with Reinforcement Learning from Human FeedbackWith Chris Olah, Liane Lovitt and Zac Hatfield-Dodds
- AI-to-AI Code Reviews of GitHub Pull RequestsWith Cameron McKinnon, Carol Chen, Chris Olah, Ethan Perez, Liane Lovitt and Samuel R. Bowman
- Predictability and Surprise in Large Generative ModelsWith Chris Olah, Liane Lovitt and Zac Hatfield-Dodds
- Discovering Language Model Behaviors with Model-Written EvaluationsWith Cameron McKinnon, Chris Olah, Ethan Perez, Liane Lovitt, Samuel R. Bowman and Zac Hatfield-Dodds
Sources and credits
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- Catherine Olsson, MIT computer + cognitive science
- People – Winawer Lab
- PhD or programming? Fast paths into aligning AI as a machine learning engineer, according to ML engineers Catherine Olsson & Daniel Ziegler
- Catherine Olsson ’08 - Lakeside School
- A General Language Assistant as a Laboratory for Alignment
- Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
- Constitutional AI: Harmlessness from AI Feedback
- BlackboxNLP 2022 | Analyzing and interpreting neural networks for NLP
- Normalization by orientation-tuned surround in human V1-V3
- Claude Founder House San Francisco | Anthropic





