Apollo Research
Research Scientist (Control)
London & San Francisco · Full-time
H1B sponsorship available$100k-$200kDetected 217 days ago
PythonSQLMachine LearningLLMsLogisticsResearch
About the role
- Join our new AGI safety product team and help transform AI control research into practical tools that directly reduce risks from AI.
- Our monitoring research agenda attempts to translate compute into safety at scale.
Responsibilities
- TLDR: you will design & implement control protocols (see e.g. [Greenblatt et al, 2023] ) and test them on real-world production systems at scale.
- Design and conduct experiments to test monitor effectiveness across different failure modes and agent behaviors
- Build and maintain evaluation frameworks to measure progress on monitoring capabilities
- Build and maintain high-quality datasets to train and test monitors on
- Monitor Design & Optimization
- Develop & maintain a comprehensive library of monitoring prompts tailored to specific failure modes (e.g., security vulnerabilities, goal misalignment, deceptive behaviors)
- Design and test hierarchical monitoring architectures and ensemble approaches
- Optimize log pre-processing pipelines to extract relevant signals while minimizing latency and computational costs
- Relocation support and visa fees (if applicable)
Requirements
- 2+ years of experience conducting empirical research with large language models or AI systems
- Strong experience with AI coding agents.
- Experience with LLM-as-a-judge setups or AI monitoring more broadly
- Demonstrated ability to work independently on open-ended research problems
- You will join a small team and will have significant ability to shape the team & tech, and have the ability to earn responsibility quickly.
- You enjoy the challenge of translating theoretical AI risks into concrete detection mechanisms.
Nice to have
- Familiarity with AI safety concepts, particularly agent-related risks
- Experience fine-tuning language models or working with smaller open-source models
- Publications or contributions to AI safety or ML research
- Experience with production log systems or production log analysis
Skills
- The rapid rise in AI capabilities offer tremendous opportunities, but also present significant risks.
Compensation
- 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)
- Salary: 100k - 200 GBP (~150k - 270k USD)
Benefits
- This role offers market competitive salary, equity, and competitive benefits.
- Flexible work hours and schedule
- 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)
Company info
- The Product team is a new team.
- Especially early on, you will work closely with Marius Hobbhahn (CEO & currently leads the monitoring team), Victor Gillioz (Research Scientist), Monika Jotautaitė (Research
- Jeremy Neiman, Zak Walters, Zen van Riel, and Srdjan Miletic.
- Furthermore you will interact with our other SWEs and researchers, since we intend to be "our own customer" by using our products internally for our research work.
- You can find our full team here.
- We are currently building Watcher, a monitoring tool for coding agents.
- 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.
Equal opportunity
- Equality Statement: Apollo Research is an Equal Opportunity Employer.
- Equal Opportunity Employer.
Visa & Work Authorization
- 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.
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This listing is sourced directly from Apollo Research's careers page and normalized into a canonical job model.