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Research and product systems

AI Researcher

This role sits at the boundary between research, product judgment, and system design. You will help define what high-quality AI visibility analysis actually means, design evaluation methods that survive contact with reality, and turn that work into product behavior teams can trust.

Applications opening soon

Role summary

Own how Chatobserver evaluates AI answers, citations, and narrative quality so the product can tell the truth about visibility inside AI search.

Why this role exists

The category is moving faster than the measurement standards around it. We need someone who can turn vague claims about AI visibility into rigorous, usable, product-grade judgment.

First 90 days

Build an initial evaluation map for the core prompt-answer-citation workflow.

Why this role exists

The category is moving faster than the measurement standards around it. We need someone who can turn vague claims about AI visibility into rigorous, usable, product-grade judgment.

What you will work on

  • Design evaluation frameworks for answer quality, citation behavior, positioning, and sentiment.
  • Work closely with product and engineering to turn research insights into concrete product logic and operator-facing workflows.
  • Investigate failure modes in extraction, normalization, and interpretation across assistants and prompt types.
  • Build the internal research habits, benchmarks, and review standards that keep the product credible as models change.

What a strong fit looks like

  • A strong research instinct paired with the ability to operationalize findings into product or system behavior.
  • Comfort reading raw model output closely and separating signal from noise without hand-waving.
  • Evidence of work in applied LLMs, information retrieval, evaluation, search relevance, or adjacent research problems.
  • Clear writing and the ability to explain why a method is trustworthy, not just interesting.

What will excite you here

  • Turning ambiguous AI behavior into repeatable evaluation logic.
  • Working across product, engineering, and research instead of staying confined to a narrow experimentation lane.
  • Building the measurement layer for a category that still lacks strong standards.

First 90 days

  1. 01Build an initial evaluation map for the core prompt-answer-citation workflow.
  2. 02Identify the highest-value reliability gaps in current analysis outputs.
  3. 03Ship at least one improvement that materially raises trust in reported AI visibility signals.

Hiring process

The process is intentionally short, direct, and anchored in the real work.

  1. 1

    Apply

    Send us your background, relevant work, and why this role makes sense for you.

  2. 2

    Foundational conversation

    A focused conversation about your work, your judgment, and the role itself.

  3. 3

    Role-specific deep dive

    A discussion or exercise that looks like the actual work more than a generic interview loop.

  4. 4

    Founder conversation

    A final conversation on standards, ambition, and what success would look like here.

  5. 5

    Decision

    We close the loop clearly and move quickly once there is conviction.

Need context before you apply? [email protected]

AI Researcher

We are not taking applications for this role yet. We will update this page when it opens.

Questions in the meantime? Email [email protected].