You will develop and structure high-level machine learning evaluation tasks that probe the edges of current AI reasoning. The work centers on identifying gaps in automated systems by crafting problems rooted in specialized domains—ones that cannot be solved with general ML knowledge alone.
Key Responsibilities
- Formulate original, research-level machine learning challenges based on your deep domain expertise
- Create evaluation frameworks that demand insight beyond conventional modeling techniques
- Review and analyze AI-generated solutions for technical accuracy, logical soundness, and innovation
- Pinpoint failure points in AI outputs and clearly articulate the missing knowledge or reasoning
- Document the conceptual barriers, required expertise, and expected shortcomings for each task
Qualifications
You must have advanced training in a technical or scientific field connected to machine learning. A graduate degree (MS or PhD) is strongly preferred. You should be deeply engaged with current research frontiers and understand where standard ML approaches break down.
Proficiency in core ML practices—including model selection, feature engineering, and evaluation metrics—is essential. You must also demonstrate exceptional clarity in written explanation, with the ability to distill complex technical concepts into precise, unambiguous language.
This is an independent contractor (1099) role, fully remote, with flexible scheduling between 10 and 40 hours per week depending on project availability. Compensation ranges from $200 to $400 per hour, based on domain and experience level.
