Lead Research Scientist/Engineer (Embedded Evaluations)

Location: London & San Francisco Workplace: onsite 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 embeddedevaluationplans. KEY RESPONSIBILITIES If embedded evaluations happen, you will be on one of the following teams: On-site embedded evaluators: This team will spend weeks or months embedded at a frontier AI lab, where they will conduct interviews, review the lab’s evidence, gather new evidence, and write a report. They 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: This team will be the force-multiplier behind embedded evaluations. They will prepare on-site evaluators before they are embedded and support them while they are embedded. They will do the conceptual work to determine what evidence would support a safety case, and write the playbook for on-site evaluators to optimize how fast they can gather evidence. They will develop tooling for embedded evaluators, both ahead-of-time and urgently on demand. They will be the bridge connecting fundamental research and the on-site evaluators. For example, they 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. We assess demonstrated ability, including through independent research and nontraditional paths; prior formal management titles are not required. Technical safety research judgment: You have a strong track record of producing or leading technically rigorous research relevant to understanding and reducing risks from advanced AI. Your background might include alignment, interpretability, model evaluations, control, adversarial ML, or post-training. You understand modern model training well enough to interrogate a lab’s methods and learn unfamiliar pipelines quickly. Deep experience with frontier RL or post-training is valuable but not required. Research leadership and delivery: You have led ambitious technical projects end-to-end under hard deadlines and managed or closely supervised strong individual contributors (ICs). You can set research priorities, delegate meaningful ownership, review technical work, and keep a team focused on the questions that matter most. 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. Strong understanding of scheming: 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. 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. Professionalism: You can represent Apollo independently in sustained, high-stakes technical collaborations.  Your professional conduct is unimpeachable. You know when to tactfully resolve disagreements with partners and when to stand firm in negotiations, and you are excellent at both of those. Mission commitment: Apollo estimates that RSI will happen soon, and we are extremely concerned that AGI will catastrophically not be aligned with human values. Hence we have an “intense, but in a good way” culture . We will ask a lot from you, but in return you will personally have a large impact on loss-of-control risk. NICE TO HAVES Software engineering skills: Strong software engineering skills correlate highly with effective execution, even in an era of AI agents. Our entire stack uses Python. Prior embedded expertise: You have previous experience in professional services like auditing or consulting. You’ve learned lessons from working on-site with clients that will generalize to working with frontier AI labs. 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. BENEFITS This role offers a market-competitive salary, equity, and competitive benefits. Depending on your experience, you may be offered a Lead or individual contributor position. Salary: Recognised experts who can lead projects of significant scope: USD 350,000 - USD 590,000 or £230,000 - £395,000 Strong individual contributors: USD 245,000 - USD 405,000 or £160,000 - £270,000 Unlimited vacation Unlimited sick leave Up to 6 months of paid parental leave Comprehensive health, dental and vision insurance Retirement savings with competitive employer matching (e.g. 401(k) for US employees) Lunch, dinner, and snacks are provided for all employees on workdays Paid work trips, including staff retreats, business trips, and relevant conferences A yearly $1,000 (USD) professional development budget Relocation support and visa fees (if applicable). We are able to sponsor visas to the UK and US. LOGISTICS Time Allocation : Full-time Location : This is an in-person role working out of our London or San Francisco office. We prefer on-site embedded evaluators to be permanently located in San Francisco. Applied researchers can work from any of our offices. We offer flexible working hours and WFH arrangements. Remote candidates will only be considered if they are exceptional and can travel at least 1 week/month to either office. Visa sponsorship: We sponsor visas in both the UK and US. Sponsorship isn't guaranteed for every role or candidate, but if we make you an offer, we'll work with you to find the right visa route ABOUT APOLLO RESEARCH The rapid rise in AI capabilities offers tremendous opportunities, but also presents significant risks. At Apollo Research, we’re primarily concerned with risks from Loss of Control, i.e. risks coming from the model itself rather than e.g. humans misusing the AI. We’re particularly concerned with deceptive alignment / scheming, a phenomenon where a model appears to be aligned but is, in fact, misaligned and capable of evading human oversight. We work on the detection of scheming (e.g., building evaluations and novel evaluation techniques), the science of scheming (e.g., model organisms and the study of scaling trends), and scheming mitigations (e.g., control). We closely work with multiple frontier AI companies, e.g. to test their models before deployment and collaborate on fundamental research. At Apollo, we aim for a culture that emphasizes truth-seeking, being goal-oriented, giving and receiving constructive feedback, and being friendly and helpful. If you’re interested in more details about what it’s like working at Apollo, you can find more information here. ABOUT THE TEAM Alex Meinke leads the science of scheming team’s research agenda with guidance from Marius Hobbhahn, though team members lead individual projects. You will mostly work with the science of scheming team as well as our team of software engineers, but you will likely sometimes interact with the governance team to translate technical knowledge into concrete recommendations. You can find our full team here. Equality Statement: Apollo Research is an Equal Opportunity Employer. We value diversity and are committed to providing equal opportunities to all, regardless of age, disability, gender reassignment, marriage and civil partnership, pregnancy and maternity, race, religion or belief, sex, or sexual orientation.  How to apply: Please complete the application form with your CV. The provision of a cover letter is optional but not necessary. Please also feel free to share links to relevant work samples. About the interview process: Our multi-stage process includes a screening interview, 3 technical interviews, and a final interview with Marius (CEO). The technical interviews will be closely related to tasks the candidate would do on the job. There are no LeetCode-style general coding interviews. If you want to prepare for the interviews, we suggest (a) thinking about what embedded evaluators should investigate, (b) building a tool that would be useful for embedded evaluators and running it on suitable proxy data, and/or (c) writing a report based on mock findings.  Your Privacy and Fairness in Our Recruitment Process: We are committed to protecting your data, ensuring fairness, and adhering to workplace fairness principles in our recruitment process. To enhance hiring efficiency, we use AI-powered tools to assist with tasks such as resume screening. These tools are designed and deployed in compliance with internationally recognized AI governance frameworks. Your personal data is handled securely and transparently. We adopt a human-centred approach: all resumes are screened by a human and final hiring decisions are made by our team. If you have questions about how your data is processed or wish to report concerns about fairness, please contact us at info@apolloresearch.ai.