How to apply for Senior Cross-Cutting Researcher

About GiveWell

GiveWell is a nonprofit that researches the most cost-effective ways to save and improve lives, and directs donations to the programs it believes will do the most good. It has grown from directing $1.5 million in 2010 to more than $400 million in 2025, with total grantmaking likely to approach or exceed $1 billion this year. Working there means your research directly informs how tens of thousands of donors allocate real funding at a large and growing scale.

About the role

This is a senior researcher role on GiveWell's Cross-Cutting team, which handles methodological questions, research quality, and big-picture problems that do not fit inside any single program area. The work includes retrospective evaluations of completed grants (lookbacks), red teaming grantmaking areas, gathering on-the-ground quantitative and qualitative information to check what grantmaking might be missing, and exploring ways to use AI to improve the research process. The output of this role shapes funding decisions across GiveWell's entire portfolio.

A typical day

A typical day might involve scoping a lookback on a completed grant, reviewing quantitative and qualitative data from the field, and writing up findings that could change how GiveWell funds a program area. You might also spend time testing an AI tool for part of the research process or discussing a methodological question with researchers across teams. Exact routines are not described in the job details, so ask about team cadence and how cross-cutting work is prioritized.

Who GiveWell is looking for

• Has strong quantitative research skills and can pressure-test evidence, cost-effectiveness models, and grantmaking assumptions across different global health and development areas.
• Is comfortable with open-ended, cross-cutting problems rather than deep specialization in one program area, and can define the question as well as answer it.
• Has experience with retrospective evaluation, red teaming, or similar adversarial review of research and funding decisions.
• Can work remotely and independently in a US-based full-time role, and is interested in using AI to improve research processes.

Tips for this application

• Read GiveWell's published research on its website before applying, especially any public write-ups of lookbacks, red teaming, or cross-cutting work, and reference specific examples in your application.
• Show that you understand the difference between program-specific research and cross-cutting research. Explain why you want to work on methodological and portfolio-wide questions rather than one cause area.
• Give concrete examples of times you have challenged or stress-tested a research finding, model, or funding recommendation, and what changed as a result.
• If you have used AI in a research workflow, describe exactly what you did and what worked or failed, since AI for research improvement is named as part of the team's work.
• Note in your application that you have read the disclaimer about 80,000 Hours' funding relationship with Coefficient Giving and GiveWell, and that you are comfortable with it.

What to cover in your cover letter

['Your track record on methodological or cross-cutting research problems, with specific examples of questions you framed and answered.', 'Experience with retrospective evaluation, red teaming, or auditing research and grantmaking decisions, and what you found.', "How you would gather on-the-ground quantitative and qualitative information to check what GiveWell's grantmaking might be missing.", 'Your concrete experience or plans for using AI to improve a research process, with honest detail about limitations.']

Research before applying

• Read GiveWell's research pages and any public posts about the Cross-Cutting team's work, including lookbacks and red teaming.
• Understand GiveWell's cost-effectiveness approach and how it decides which global health and development programs to fund.
• Review GiveWell's growth in funding directed, from $1.5 million in 2010 to more than $400 million in 2025 and the expected approach to or exceed $1 billion this year, and think about what that scale means for research quality.
• Check the funding relationship between 80,000 Hours and Coefficient Giving, and between Coefficient Giving and GiveWell, so you can speak to it if asked.

Likely interview topics

Based on the job description, expect questions about:

• How you would design a lookback evaluation of a completed GiveWell grant, including what data you would collect and how you would judge success.
• A time you red teamed or stress-tested a research area or funding decision, and what you would do differently now.
• How you would check whether GiveWell's grantmaking is missing important information, using on-the-ground quantitative and qualitative evidence.
• How you would use AI to improve GiveWell's research process, including where it would help and where it would not.
• How you prioritize among multiple cross-cutting research questions when the portfolio spans many program areas.

Common mistakes to avoid

• Applying as if this were a program-specific research role. The Cross-Cutting team works on methodology, research quality, and portfolio-wide problems, not one cause area.
• Making generic claims about doing good without engaging with GiveWell's actual cost-effectiveness methods and published research.
• Claiming AI experience without concrete examples of what you built, tested, or learned, since AI for research improvement is explicitly part of the team's work.