Research Scientist

FutureSearch is looking for exceptional Research Scientists to evaluate and improve state-of-the-art forecasting and agentic LLM web research. We are an elite team of engineers and researchers at the frontier of AI epistemics. We have the best publicly available forecaster, with a public track record in forecasting tournaments at evals.futuresearch.ai and markets.futuresearch.ai. We publish papers, such as our ICLR Workshop paper in Jan 2026, and benchmarks like Deep Research Bench and Bench to the Future. We use our frontier research tools to research the future, such as co-authoring the AI 2027 Timelines Forecast. We work with frontier labs on research, evaluation, and training. You are a talented researcher with background in math, physics, CS, or an equivalent quantitative field. You are currently studying modern AI systems, and you seek to understand how they are and will affect the world. You can start with an open problem – determining how likely it is that a given type of information produced by an LLM is true, for example – devise an innovative solution, and build great evals to convince us that it’s working. The strongest candidates will have experience with Researching LLMs Researching epistemics: search, forecasting, judgment, establishing truth of claims, etc. Creating benchmarks or datasets to evaluate AI systems The ideal candidate has a burning desire to improve AI epistemics in a way that improves humanity’s decision making in the near-term. FutureSearch is fully remote. With the team spread across SF, NYC, London, Berlin, and Czechia, we prioritize traveling to meet 4-6 times a year. We have flexible hours, and operate in a high trust environment. Our salary range is $125,000 to $225,000, varying primarily by country. We offer higher-than-average equity. You should expect a total compensation package that is near the top for any seed-stage startup, with large room to grow. We offer full benefits, varying based on your country, including: healthcare, paternity/maternity leave, equipment, and travel expenses. #J-18808-Ljbffr