Effective Altruism Global
Member of Technical Staff
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AI Safety Research
Cambridge, United Kingdom
Geodesic
Research
The shortest path to impact
About Us
We build robust initialisations for alignment—base models shaped through pre- and midtraining to hold up under capabilities reinforcement learning.
Geodesic Research is a UK-based technical AI safety organisation focused on compute-intensive alignment research. Our seminal work on alignment pretraining showed that you can bake alignment priors into base models, and the broader field is now converging on the approach.
Long-horizon capabilities reinforcement learning is emerging as a critical and underexplored threat to alignment—degrading alignment properties across evals and selecting for behaviours like metagaming, sycophancy, and reward hacking. Our agenda is to design midtraining and early post-training interventions that create initialisations where alignment persists through the rest of training.
Research Taste
01
Conceptually Simple
We focus on conceptually simple, data-centric interventions—document mixes, filtering, declarative midtraining—that benefit from scale and slot into existing training pipelines without bespoke infrastructure.
02
Uniquely Positioned
Philanthropic funding from Coefficient Giving and a compute partnership with the UK AI Security Institute put us among the few non-lab actors who can replicate the full midtraining, SFT, and RL stack at scale. We have no commercial stake in any particular alignment method, leaving us free to investigate the full picture.
03
Frontier Impact
Our target audience is model training teams at frontier labs. We design interventions that can be profiled, packaged, and handed off—taking the shortest path to advising on their production training stacks.
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Research Directions
Can alignment hold up through capabilities RL?
01
Alignment Pretraining
Our seminal work showed that AI discourse causes self-fulfilling (mis)alignment—and that you can shape these priors during pretraining. Frontier labs are now converging on this approach: Anthropic's recent Teaching Claude Why and Model Spec Midtraining both lean on the alignment-priors framing we pioneered.
Learn more
02
Misalignment Quarantining
Post-training on imperfect data can broadly corrupt a base model's alignment. We will explore shaping base models with declarative midtraining documents that aim to establish an explicit context boundary around unsafe behaviour—so that benign features might generalise while the misalignment underneath stays quarantined.
03
Adversarial Robustness to Capabilities RL
Long-horizon capabilities RL may degrade alignment in ways pretraining alone cannot prevent. We will stress-test midtraining and early post-training interventions against agentic RL with misspecified rewards—aiming to surface which methods could produce truly robust initialisations and which might break down under pressure.
Key Papers
Alignment Pretraining: AI Discourse Causes Self-Fulfilling (Mis)alignment
Read paper
Blog Posts
Announcing Geodesic Research
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The Team
Founded in Cambridge, UK
Cameron Tice
Co-Founder & Executive Director
Marshall Scholar at the University of Cambridge, where he completed his MPhil on automated research with LLMs for computational psychiatry. A former Research Manager for the ERA:AI fellowship.
Puria Radmard
Co-Founder & Technical Director
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Former ERA:AI fellow and University of Cambridge PhD student. Previously a machine learning engineer at raft.ai and a private equity quantitative strategist at Goldman Sachs.
Alexandra Narin
Head of Operations
Cofounder of UK AI Forum. Previously, a Experimental Neuroscience researcher at UCL and the Head of Grants for a Biotech company.
Edward Young
Founding Member of Technical Staff
Former researcher on AISI's Safeguards team and ERA:AI Fellow. Completed a Computational Neuroscience PhD at the University of Cambridge.
Kyle O'Brien
Founding Member of Technical Staff
Leads the alignment pretraining research agenda and has developed strong relationships with UK AI Security Institute through previous research on Deep Ignorance. Previously at EleutherAI and Microsoft.
Nathalie Kirch
Member of Technical Staff
PhD student in computer science at Imperial College London and King's College London, researching mechanistic interpretability and robustness in LLMs. Previously a MATS Research Scholar, LASR fellow, and ERA:AI fellow.
Mentors
Guided by leading researchers
Alex Turner
Google DeepMind
Tomek Korbak
OpenAI
Alex Cloud
Anthropic
David Demitri Africa
UK AI Security Institute
Join Us
Help build the future of AI safety
Geodesic Is Hiring Four Additional Members Of Technical Staff.
We're looking for technical staff with experience across the ML and alignment research stack: multi-GPU / HPC training and evals experience, deep familiarity with data-centric alignment methods, and an insatiable desire to improve the outcomes of developing superintelligence.
If this sounds like you, please apply.
Job Details Apply Now
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