Growth Design Reviewer

A Growth Design Reviewer evaluates AI-generated design treatments created for growth team to determine if a proposed change will move product metrics. This is a narrow evaluation and labeling role—not a traditional hands-on, creative product design position. Contractors will not create comps, own surfaces, or build original features. Instead, they will use their human product sense and growth judgment to score treatments against a written design bar, provide brief written critiques on contested calls, and train an automated AI reviewer through consistent feedback.

Typical Activities:

• Reviewing AI-generated (Claude) design treatments against a written design bar and rule set using internal review tool.
• Scoring and labeling treatments, providing short, clear, and actionable written critiques for contested decisions.
• Flagging recurring design mistakes, gaps in the design bar, or edge cases that current rules don't cover.
• Thinking deeply about alternative potential solutions when presented with narrow design directions.
• Joining a short weekly calibration sync with Head of Design for the Growth Org.

Ideal Candidate Profile:

The target profile is relatively narrow. Candidates should have actually worked on a growth or experimentation team and should have direct experience using design to influence measurable product outcomes.

Must-Haves:

• Direct Growth Team Track Record: Must have worked directly on a growth or experimentation-heavy product team (not adjacent to one). This is the single strongest signal for fit.
• A/B Testing Mastery: Hands-on experience running, designing, or analyzing A/B tests and discussing results using specific performance metrics.
• Incremental Optimization Focus: Instincts honed in incremental conversion work, page/flow changes, and metric-driven optimization.
• No-Ego Evaluation Mindset: Comfortable evaluating and grading AI output repeatedly without needing creative ownership.
• Actionable & Concise Writing: Able to distill product sense into brief, clear, and actionable written feedback that lands instantly for others.
• Comfortable Being Wrong in Public: Reasons directly from data and is comfortable having metric assumptions tested.
• Nice-to-Haves: Experience writing or maintaining design guidelines, heuristics, or review rubric.