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Research Scientist/Engineer (Science of Scheming)

ApolloresearchLondon & San Francisco🇬🇧United KingdomPosted 13 Feb 2026

Quick overview

SeniorityMid Senior
Employment typeFull Time
Work modeOn Site
LocationLondon & San Francisco, United Kingdom
Posted8 months ago
ConcretePython

Job description

Application deadline: We are conducting interviews actively and aim to fill this role as soon as we find someone suitable.
ABOUT THE OPPORTUNITY
We want to develop a “Science of Scheming”. The goal is ambitious and we’re looking for Research Scientists and Research Engineers who are excited to build a new hard science from the ground up.
YOU WILL HAVE THE OPPORTUNITY TO
- Collaborate with leading AI developers. We partner with multiple labs, giving you access to a breadth of models that no single AI lab could offer. Through long-term research collaborations, your work directly impacts how the most capable AI systems are built and deployed.
- Deeply study the RL dynamics that lead to the emergence of reward-seeking, evaluation awareness or misaligned preferences. Design and train model organisms, and scale your insights to frontier systems.
- Work towards “Scaling laws of scheming”. Build the empirical foundations to predict how scheming risks evolve as models scale in capability.
- Develop novel and ambitious evaluation techniques that have a chance of scaling to highly evaluation aware models.
- Deep dive into AI cognition. Discover patterns in the reasoning processes of frontier AI systems that no one else has ever observed before.
Note:  We are not hiring for interpretability roles.
KEY REQUIREMENTS
A diverse range of skill sets will be required to drive our research agenda forward and we don’t expect any single candidate to fulfill all the characteristics below. That being said, a successful candidate likely displays excellence at one or several of the following:
- Fast-paced empirical research: You can design and execute experiments. You always strive to speed up iteration cycles and relentlessly drive progress towards the next empirical milestone.
- Conceptual insights about scheming: You have deeply thought about the problem of AI scheming and are familiar with all the relevant literature. You are able to turn vague and undefined concepts into concrete and insightful experiment proposals.
- Software engineering skills: Strong software engineering skills correlate highly with effective execution, even in an era of AI agents. Our entire stack uses Python.
- Intense interest in AI progress: You always stay up to date on the latest model releases, and continuously tinker with new and creative AI workflows to speed up your work. You are fascinated by AI cognition and actively spend time trying to understand how they think.
- Experience RL-training LLMs: You have hands-on experience in training LLMs via reinforcement learning. You have encountered and resolved countless painful issues from GPU failures to debugging learning instabilities.
- Strong analytical skills: You bring rigorous quantitative chops from working on fields such as scaling laws in LLMs, statistical physics, dynamical systems, applied statistics etc. You're comfortable building mathematical models of empirical phenomena and know how to extract signal from noisy data.
We want to emphasize that people who feel they don’t fulfill all of these characteristics but think they would be a good fit for the position, nonetheless, are strongly encouraged to apply. We believe that excellent candidates can come from a variety of backgrounds and are excited to give you opportunities to shine. We don’t require a formal background or industry experience and welcome self-taught candidates.

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