Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Join us to build the 'Science of Scheming' in AI research and development.
- Company: Apollo Research, a leader in AI risk management and innovative research.
- Benefits: Competitive salary, equity, unlimited vacation, and professional development budget.
- Other info: Diverse backgrounds welcome; no formal experience required.
- Why this job: Make a real impact on AI safety and work with cutting-edge technology.
- Qualifications: Passion for AI, strong analytical skills, and experience in reinforcement learning.
The predicted salary is between 80000 - 120000 ÂŁ per year.
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 fulfil 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.
BENEFITS
This role offers market competitive salary, equity, and competitive benefits.
- Salary: 100k - 200k GBP (~135k - 270k USD)
- Flexible work hours and schedule
- Unlimited vacation
- Unlimited sick leave
- 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
LOGISTICS
Time Allocation: Full‑time
Location: The office is in London, and the building is shared with the London Initiative for Safe AI (LISA) offices. This is an in‑person role. In rare situations, we may consider partially remote arrangements on a case‑by‑case basis.
Work Visas: We can sponsor UK visas.
ABOUT APOLLO RESEARCH
The rapid rise in AI capabilities offers tremendous opportunities, but also presents significant risks. Apollo Research is primarily concerned with risks from Loss of Control, i.e. risks coming from the model itself rather than 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
The current evals team consists of Jérémy Scheurer, Alex Meinke, Bronson Schoen, Felix Hofstätter, Axel Højmark, Teun van der Weij, Alex Lloyd and Mia Hopman. Alex Meinke coordinates the research agenda with guidance from Marius Hobbhahn, though team members lead individual projects. You will mostly work with the evals 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, a take‑home test (approx. 2.5 hours), 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 working on hands‑on LLM evals projects (e.g. as suggested in our starter guide); such as building LM agent evaluations in Inspect.
Research Scientist/Engineer (Science of Scheming) in London employer: COL Limited
Contact Detail:
COL Limited Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Research Scientist/Engineer (Science of Scheming) in London
✨Tip Number 1
Get your networking game on! Reach out to folks in the AI and research community, especially those connected to Apollo Research. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Prepare for those interviews like it's a science experiment! Brush up on your knowledge of RL dynamics and scheming concepts. The more you know, the better you'll impress during those technical interviews.
✨Tip Number 3
Show off your hands-on experience! If you've worked on LLMs or have cool projects under your belt, make sure to highlight them. Real-world examples can set you apart from the crowd.
✨Tip Number 4
Don't forget to apply through our website! It's the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Research Scientist/Engineer (Science of Scheming) in London
Some tips for your application 🫡
Show Your Passion for AI: Let us see your excitement for AI in your application! Share any projects or experiences that highlight your interest in AI progress and cognition. We want to know what makes you tick!
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with our needs. Highlight your empirical research experience, software engineering skills, and any relevant work with reinforcement learning. A tailored CV can really make you stand out!
Keep It Clear and Concise: When writing your application, clarity is key! Use straightforward language and avoid jargon where possible. We appreciate a well-structured application that gets straight to the point.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows you’re serious about joining our team!
How to prepare for a job interview at COL Limited
✨Know Your Stuff
Make sure you’re well-versed in the latest research and developments in AI scheming. Familiarise yourself with relevant literature and be ready to discuss your insights and how they can contribute to the role.
✨Show Off Your Skills
Prepare to demonstrate your empirical research skills. Bring examples of experiments you've designed or executed, and be ready to discuss how you’ve tackled challenges in your past projects, especially in reinforcement learning.
✨Get Hands-On
Since the role involves practical applications, work on some hands-on LLM evaluation projects before your interview. This will not only give you confidence but also provide concrete examples to discuss during your technical interviews.
✨Be Ready for Collaboration
This position involves working closely with a team of engineers and researchers. Be prepared to talk about your experience in collaborative environments and how you handle feedback and constructive criticism.