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ML Engineer

Job in Winnipeg, Manitoba, Canada
Listing for: Great Value Hiring
Full Time position
Listed on 2026-06-09
Job specializations:
  • Research/Development
  • IT/Tech
Salary/Wage Range or Industry Benchmark: 80 - 90 CAD Hourly CAD 80.00 90.00 HOUR
Job Description & How to Apply Below
Research Experts for Frontier AI/ML Open Questions [$80-$90/hr]
Exceptional PhD students and Post Docs 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 Post Docs 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

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