# Research Scientist/Engineer (Fundamental Science) at Apollo Research

Apollo Research is hiring a Research Scientist/Engineer (Fundamental Science) for a remote role open to applicants in United Kingdom. Level rates it Builds AI ●●●●; you can [apply on Apollo Research](https://jobsbylevel.com/go/dca560d1-a27e-4d97-9158-a743e4205199?utm_medium=ai-agent).

AI Level 4, AI centrality 100 out of 100. Remote (London & San Francisco).

## Details

- Company: [Apollo Research](https://jobsbylevel.com/companies/apollo-research)
- AI level: AI Level 4 (score 100 out of 100)
- Location: Remote (London & San Francisco)
- Posted: October 30, 2026
- Apply: https://jobsbylevel.com/go/dca560d1-a27e-4d97-9158-a743e4205199?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=research-scientist-engineer-fundamental-science-at-apollo-research-c7697f

## Prepare for this job

The job page lists 5 likely interview questions for this offer, with a CV checklist: https://jobsbylevel.com/jobs/research-scientist-engineer-fundamental-science-at-apollo-research-c7697f

## Description

Application deadline: We are conducting interviews actively and aim to fill this role as soon as we find someone suitable. ABOUT THE OPPORTUNITY The OpenAI/Hugging Face incident may be the biggest warning shot we get before catastrophic loss-of-control. In its aftermath, Anthropic and OpenAI committed to giving embedded evaluators an unprecedented level of access. Embedded evaluations are the best existing method to find out whether alignment is on track. With OpenAI predicting an automated researcher by March 2028, we don't have time to start with an MVP embedded evaluation and iteratively improve it. The first embedded evaluations will set the standard and we have to get it right. As a fundamental researcher, your ground-breaking discoveries will make our embedded evaluations better. For example, our fundamental science team's paper “Measuring Reward-Seeking via Contrastive Belief Updates” can be adapted by embedded evaluators. We want more breakthroughs like this. We are also developing 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. KEY RESPONSIBILITIES 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 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. AI power-user: You’re capable of using AI to accelerate your work, technical or otherwise. You have experience using different models, know which ones to use for which tasks, when not to use AI, and always experiment with new AI workflows. Conceptual thinking and writing : You are familiar with prior work on AI scheming, from classic literature like Is Power-Seeking AI an Existential Risk? to modern research. You have done enough deep thinking about the problem to generate some original ideas. Given vague and underspecified concepts, you come up with clarifying insights, clever ideas for experiments, and detailed plans. To explain your work, you can produce high-quality scientific writing, figures, blog posts, microsites, and tweet threads. 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. Mission commitment: Apollo estimates that RSI will happen soon, and we are extremely concerned that AGI will catastrophically not be

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