ML Engineer

Actively Reviewing the Applications

Great Value Hiring

Noida Full-Time 4–8 years

Posted 3 days ago • Apply by June 11, 2026

Job Description

Research Experts for Frontier AI/ML Open Questions [$80-$90/hr]

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

Role Responsibilities

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
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

Good Candidature

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

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

Eligibility Requirement

You will need to fill a short form in order to be eligible for this role:

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