Mercor is working with a leading intelligence AI lab to identify the most important open questions in core AI/ML fields and to build structured knowledge bases that could meaningfully accelerate progress over the next decade. We’re looking for exceptional PhD students and PostDocs with a clear point of view on what problems truly matter in their field and the depth to define how those problems could be tackled.
Eligibility Requirement
You will need to fill a short form in order to be eligible for this role: You will see this in addition to the AI interview in your application process. Below is guidance for what you will need to have in order to fill the form:
Consider the biggest open questions in your field, for example, the 10–15 questions where a breakthrough would make headline news. From this set, select those closest to your area of expertise: questions within or adjacent to your specialty, or those where you could mentor an expert toward meaningful progress.
Specifically, we are looking for questions where:
- A major breakthrough would be widely recognized as transformative (e.g., headline news in Nature, Science, or top field-specific venues)
- Meaningful progress is plausible within the next decade (not purely speculative or dependent on unknown technology)
- The question is concrete enough that progress can be evaluated (avoid umbrella questions like “How does the ML work?”)
- You have the relevant expertise to assemble a comprehensive knowledge base directly relevant to the question
What You’ll Do
1. Identify high-impact open questions
- Propose major open questions where a breakthrough would be transformative
- Focus on problems that are concrete, tractable, and close to your expertise
2. Build a knowledge base for selected questions
- Seminal papers, key datasets, methods, recent advances, and “hidden gems”
- Assume an extremely strong expert all knowledge up until 6 months ago (1st October, 2025)
Time commitment: ~8–16 hours per selected question
Who We’re Looking For
- PhD candidates or PostDocs from top-tier institutions
- Deep expertise in AI/ML/Engineering
- Strong judgment about significance, tractability, and research quality
- Ability to synthesize large bodies of literature into clear learning paths
- Openings: ~50 experts per domain
Expected Outputs
- Clearly articulated, high-impact open research questions
- Structured reading lists (~30–100 sources per question)
- Brief expert commentary on why each source and approach matters