An Unofficial Guide to Prepare for a Research Position Application

2026-01-20 · Source: Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Intermediate, quick

Summary

Sakana AI has released an unofficial guide detailing its criteria for evaluating research position candidates, authored by Stefania Druga, Luke Darlow, and Llion Jones. The guide emphasizes a core principle of understanding over mere implementation, noting that while many candidates can build complex systems, fewer can articulate the rationale behind design choices, identify limitations, or propose improvements. Key insights include the importance of asking questions that distill problem spaces, prototyping only the riskiest assumptions, pursuing depth over breadth with unconventional ideas, clear communication by stating conclusions first, and balancing engineering with creativity to achieve "good enough" results rather than endless perfection. These principles reflect Sakana AI's broader philosophy on effective AI research.

Key takeaway

For AI Researchers or Students preparing for research position applications, your focus should shift from merely demonstrating technical building skills to articulating the "why" behind your design choices. Emphasize critical evaluation of limitations and propose thoughtful improvements. This approach will distinguish your application by showcasing deep understanding and research acumen, which are fundamental to advancing AI.

Key insights

Deep understanding and critical thinking are paramount in AI research applications, surpassing mere implementation skills.

Principles

• Prioritize understanding over implementation.
• Focus on depth, not breadth, in research ideas.
• Communicate clearly and reduce ambiguity.

Method

Candidates should distill problem spaces with incisive questions, prototype only the riskiest assumptions, and balance engineering pragmatism with creative exploration.

In practice

• Identify core uncertainties directly.
• Build only to test riskiest assumptions.
• State conclusions first in communications.

Topics

• Research Candidate Evaluation
• AI Research Principles
• Technical Communication
• Research Prototyping
• Problem Distillation

Best for: AI Researcher, AI Student, Research Scientist

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