See 🚩 Candidate Hard Gates & Red Flags for company-wide eligibility rules (NSFW, B2B, location default, English, compensation-expectations screening). What follows is specific to this role, including one explicit exception to that page's defaults.
⛔️ Top Requirements
- AI-native — active, meaningful use of AI in their actual product-building workflow (hard requirement, not a positive signal).
- Hands-on builder — prototypes, experiments and validates ideas rather than operating only through specs/coordination.
- Strong B2C product experience.
- Strong product judgment — moves from user/problem → experiment → shipped result.
- Comfortable operating with high autonomy and fast iteration.
- Demonstrates personal ownership and measurable product outcomes.
🎯 What We Are Hiring For
A hands-on, technical PM at the intersection of product, AI, data, and engineering — not a typical PM whose work is primarily roadmaps, tickets, and handing specs to engineering. Reports to Pierre Gerbaud (Product Lead). This person identifies opportunities, prototypes and tests ideas quickly using AI tools directly, and pushes experiments toward production — high agency, moves fast with minimal oversight.
⛔️ Screening Criteria
- AI-native working style — actively uses modern AI tools (Claude Code, Codex, Cursor, or similar) in their actual workflow and can explain concretely how.
- Builder / prototyping ability — personally prototypes and ships, rather than relying entirely on engineering/design to validate every idea; has shipped a real prototype, experiment, or feature end-to-end.
- B2C product depth — worked on consumer products where user behaviour, engagement, monetization, retention, or experimentation outcomes matter.
- Product judgment — can explain what problem they identified, why they prioritized it, what they personally did, what was tested/shipped, what happened, and what they learned or changed.
- Ownership — demonstrates personal ownership rather than describing only what "the team" delivered.
- Speed / experimentation — comfortable testing, learning and iterating rather than requiring long planning cycles before action.
Professional evidence is strongly preferred; an exceptional side project may still count if it shows production-level complexity, iteration, and ownership — it should not be just a landing page or toy demo.
AI usage is a hard gate for this role. Company-wide, AI usage is generally a positive signal rather than mandatory (see Candidate Hard Gates & Red Flags) — but Senior Product Manager is one of only four roles where it's an explicit requirement, alongside Product Designer, Senior Ruby on Rails Engineer, and AI Cinematic Video Editor/Creator. Do not submit a candidate who cannot show hands-on, personal AI-building experience for this role.
🚩 Red Flags
- PM work is mainly coordination, roadmaps, Jira/ticket management or stakeholder management.
- Cannot give concrete examples of personally building/prototyping/testing.
- No meaningful current AI-tool usage.
- Talks about AI conceptually but does not use it in their own workflow.
- Cannot separate personal contribution from team output.
- Vague product impact with no measurable outcome.
- Primarily enterprise/internal-tool background without convincing transferable B2C product depth.
- Depends on engineering/design for every prototype or validation step.
- Cannot explain why an experiment/product decision succeeded or failed.
💬 Recruiter Screening Questions
Use these questions during your screening call. Include a summary of the candidate's answers in your submission notes in Ashby.
- What are you doing currently, and why are you considering a move now?
- Why EverAI?
- Have you tried Candy.ai? What do you think about it?
- Our product involves AI companionship and uncensored, adult-oriented content. Are you fully comfortable building and shipping features in this category?
- Walk me through a specific feature or prototype you personally built and shipped using AI coding tools — how did you use AI to write code, analyze data, or contribute pull requests, and what was your process?
- What AI tools or techniques have you sought out and tried on your own initiative, beyond what a specific project required? How do you validate what they produce?
- Tell me about a recent product idea you personally moved from concept toward something testable or shipped. Did you prototype it yourself, what tools did you use, and how quickly did you get to validation?
- What was the highest impact (on revenue or similar) feature you've worked on?
- What was the problem, your hypothesis, the decision you made, the metric you tracked, the result, and what you learned?
- Which of your product experience is genuinely consumer-facing (B2C) rather than enterprise/internal tooling?
- What are the top 2–3 things you're looking for in your next opportunity?
- Location / compensation expectations / notice period / B2B?
- What questions do you have for me?
🎯 Strong Signals / Sourcing Guidance
Positive signals — not automatic hard gates:
- B2C entertainment, gaming, consumer subscription, or social/creator products.
- Highly experimental consumer products.
- Demonstrated side projects / prototypes shipped with AI tools.
- Technical fluency and a strong experimentation background.
- Evidence of building things independently.
- Design fluency — can prompt a decent UI to stay autonomous (valuable, not a blocker for an otherwise strong candidate).
Growth trajectory > experience. Don't over-index on seniority or years of experience — a candidate with real agency, curiosity, and momentum can be a stronger fit than someone more senior without that trajectory. A non-traditional path into PM (e.g. a developer or designer who grew into product) is welcome, not a concern.
❓ Why does a non-traditional path fit this well?
Classic PM career progression is built around not coding — writing specs, running standups, delegating to engineers. The people who've spent years doing that are the population least likely to have picked up hands-on AI-coding fluency, precisely because that wasn't their job. The people who do have that fluency right now tend to be recent converts — people who just started operating in a PM-adjacent capacity, not people with a multi-year PM career behind them.
Always ask for a portfolio, GitHub profile, repository, live project, or demo — the single best signal for AI-native building. Do not source from direct AI-companionship/AI-dating competitors (Replika, Character.AI, and similar) — this is not what we're looking for.
📁 Sourcing Guidance
| Industry | Target companies |
|---|---|
| B2C entertainment/consumer — primary (genuinely scale-up stage) | Duolingo, Discord, Whatnot, Locket, Suno |
| B2C entertainment/consumer — secondary (mature, but still runs heavy internal experimentation) | Netflix, Spotify, Snap, TikTok/ByteDance |
| Gaming — primary (fast A/B-test cultures, scale-up stage) | Voodoo, SuperPlay, Homa Games, Kwalee |
| Gaming — secondary (large/acquired studios, still experimentation-heavy) | Scopely, Zynga, King, Supercell, Miniclip, Playtika |
| Companies where PM-as-builder is the norm (closest match for the AI-native trait) | Linear, Vercel, Notion, Ramp, Retool, Replit, Cursor, Windsurf, Perplexity, early-stage YC-style startups generally |
Sourcing aid only, not an automatic qualifier or rejection rule — a strong candidate from outside these pools should still be considered. The first four pools cover background fit only, not AI-coding fluency; the PM-as-builder pool (strongest for AI-native fluency) skews B2B/dev-tools, not B2C — the two preferences may not always show up in the same candidate.
🔄 Interview Process
Follows the shared default process — see ⏳ Hiring Process for details on each stage.