How to apply for Senior LLM Researcher

About Forecasting Research Institute

The Forecasting Research Institute develops forecasting methods to improve decision-making. The team applies these methods to real problems, including work aimed at policymakers. This is a research-focused organization, not a product company.

About the role

You will apply large language models to speed up forecasting research and build the science of LLM-based forecasting. The role involves running research projects from design to completion, building LLM tools, and comparing LLM and human forecasting. You will coordinate analysts and assistants and help write research reports and academic papers.

A typical day

A typical day may involve coding LLM tools, reviewing forecasting data, and meeting with research analysts to track project progress. You might run experiments comparing LLM and human forecasts, interpret results, and draft sections of a research report. Because the role is remote and project-based, you will manage your own schedule and coordinate with the team asynchronously.

Who Forecasting Research Institute is looking for

• Strong Python coding skills, with experience building LLM-based tools or pipelines.
• Familiarity with quantitative forecasting, including benchmarks and value-of-information assessments.
• Proven project management: can run one or more research projects from design through implementation and meet deadlines.
• Clear written and verbal communication, with experience coordinating research analysts or assistants and contributing to papers or reports.

Tips for this application

• In your materials, describe specific LLM tools or pipelines you have built, including what they did and how they were evaluated.
• Show evidence of managing a research project end to end, including coordinating others and meeting deadlines.
• Demonstrate familiarity with forecasting research by citing concrete methods, benchmarks, or papers you have worked with.
• Explain how you would compare LLM and human forecasting effectiveness, since this is a core part of the role.
• Mention any experience writing research reports or academic papers, as the role includes coordinating writing.

What to cover in your cover letter

Describe your experience applying LLMs to research problems, not just using them. Explain how you have managed a research project from design to completion, including coordinating analysts or assistants. Give a concrete example of building an LLM tool for reasoning, synthesis, or evaluation. State your familiarity with quantitative forecasting and how you would approach comparing LLM and human forecasts.

Research before applying

• Read the Forecasting Research Institute website, especially any published research or reports, to understand their methods and current projects.
• Look for any public work by the institute on LLM-based forecasting or adversarial collaboration.
• Check whether the institute has published papers or blog posts on comparing human and LLM forecasting, and note the benchmarks they use.
• Find out who leads research at the institute and what their recent work focuses on, to tailor your application.

Likely interview topics

Based on the job description, expect questions about:

• How you would design a study comparing LLM and human forecasting effectiveness, including value-of-information assessments and conditional forecasting benchmarks.
• Your approach to building LLM tools that identify high-quality forecasting rationales or synthesize expert reasoning.
• How you manage a research project from design through implementation while coordinating analysts and assistants to meet deadlines.
• Your experience with quantitative forecasting methods and how you interpret results from forecasting experiments.
• How you would write up and communicate research findings to both academic and policy audiences.

Common mistakes to avoid

• Focusing only on general LLM experience without connecting it to forecasting research or evaluation.
• Claiming project management experience without giving examples of coordinating analysts or assistants on a research project.
• Ignoring the policy angle; the role explicitly aims to advance LLM-based forecasting for policymakers.