# Lead Research Scientist/Engineer (Embedded Evaluations) at Apollo Research

Apollo Research is hiring a Lead Research Scientist/Engineer (Embedded Evaluations) 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/ab536626-e9b5-4139-98cf-48caabceba09?utm_medium=ai-agent).

AI Level 4, AI centrality 90 out of 100. Remote (London & San Francisco).

## Details

- Company: [Apollo Research](https://jobsbylevel.com/companies/apollo-research)
- AI level: AI Level 4 (score 90 out of 100)
- Location: Remote (London & San Francisco)
- Posted: October 30, 2026
- Apply: https://jobsbylevel.com/go/ab536626-e9b5-4139-98cf-48caabceba09?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=lead-research-scientist-engineer-embedded-evaluations-at-apollo-research-67e841

## Prepare for this job

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## 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 publicly 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. Apollo has taken steps toward becoming an embedded evaluator at frontier labs. We want to hire experienced senior researchers with a strong track record of getting things done to execute our embedded evaluation plans . KEY RESPONSIBILITIES If embedded evaluations happen, you will be on one of the following teams: On-site embedded evaluators: You will spend weeks or months embedded at a frontier AI lab, where you will conduct interviews, review the lab’s evidence, gather new evidence, and write a report. You will execute a playbook quickly and adapt to unforeseen circumstances. We hold our embedded evaluators to the highest standards, so you will likely have to first prove yourself within Apollo before being sent into an embedded campaign. That is, you will probably start on the applied research team to gain context on embedded evaluations. For this team, we prefer candidates with the right to work in the US who can be based in San Francisco. Applied researchers: You will be the force-multiplier behind embedded evaluations. You will prepare on-site evaluators before they are embedded and support them while they are embedded. You will do the conceptual work to determine what evidence would support a safety case, and you will write the playbook for on-site evaluators to optimize how fast they can gather evidence. You will develop tooling for embedded evaluators, both ahead-of-time and urgently on demand. You will be the bridge connecting fundamental research and the on-site evaluators. For example, you might convert the methodology of “Measuring Reward-Seeking via Contrastive Belief Updates” into turnkey code and procedures. This team can work from any of our offices. Both are high-stakes teams. On-site embedded evaluators should be exceptionally strong at quick execution under high pressure, and very strong at planning and research. Applied researchers should be exceptionally strong at planning and research, and very strong at quick execution under high pressure. For both teams, we are excited for experienced candidates who have owned ambitious projects end-to-end under tight deadlines. KEY REQUIREMENTS We don’t require a formal background or industry experience and welcome self-taught candidates. Data Analysis & Pattern Recognition: You can extract signal from large, messy datasets. You're comfortable with quantitative analysis and know when qualitative assessment is more appropriate. You can identify anomalies and unexpected model behaviors. Writing for high-stakes audiences: You have experience writing about qualitative and quantitative findings for different stakeholders. You can take a pile of unorganized technical information and communicate it effectively to lab safety teams, lab leadership, governments, or the public. 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. Expertise in scheming-related topics: You have a strong conceptual understanding of ideas like metagaming, evaluation awareness, deception, and reward-seeking. You are familiar with classic literature like Is Power-Seeking AI an Existential Risk? and modern research. Hands-on

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