Signal Watch: The 100 List — Who Owns the Trust Decision

Fourteen companies scanned. Three Act, four Watch. Six downgraded. The sorting variable was not AI centrality or company stage. It was whether the design leader in this role is accountable for the moment a human decides to trust, override, or reverse what the machine produced.

I'm calling this accountability-native. Amplitude's Wave surfaces agent-generated product signals a team must decide to act on or ignore. Vanta's compliance automation generates findings a customer must trust without manually re-auditing. HackerOne's Hai agent produces vulnerability reports a security team must prioritize or dismiss. In each case, the design leader owns the surface where trust is calibrated. Not decorates. Owns.

The roles that fell off failed on decay. Ramp, Headway, Suno, Salesforce, Rubrik, Atlassian. All crossed thresholds or lacked verifiable freshness. Two roles that did clear Watch, Capital One and Adobe, are accountability-adjacent: the design leader transforms how design works with AI rather than owning a specific product trust surface. They matter, but for different reasons.

One new entry: HackerOne, elevated from outside the original target list because the decision-rights match is unusually clean.

Window Status Changes

Upgraded

• HackerOne: New → Watch. Director, AI Product Design surfaced on Ashby. Reports to CPO Nidhi Aggarwal (in seat since June 2025). Hai AI agent has 90% customer adoption. Posting date not confirmed from primary source. Watch until verified.
• Capital One: Ignore-until-trigger → Watch. Director, Product Design — AI in Experience Design posting refreshed July 20, resolving the URL conflict from Issue #4. The Battlecard reframed this as a second-slate window for an AI governance mandate across the XD organization. Geographic eligibility for SF-based candidates not confirmed from this cycle's research. Verify before outreach.

Downgraded

• Ramp: Watch → Ignore-until-trigger. Day 46. Posting still loads but no revision evidence since the June 8 datePosted. Was the cleanest Act signal in Issue #3. Trigger: repost, language revision, or hiring-manager signal.
• Headway: Watch → Ignore-until-trigger. Careers page lists three design roles, none Design Director. The Ashby URL remains technically reachable but didn't expose role content this cycle. Was "strong but authority-sensitive" in Issue #3. Trigger: role reappears on careers index, or hiring-manager post.
• Suno: Watch → Ignore-until-trigger. Day 94. Posting loads but 94 days without fill at a 200-person company is either a very patient search or a deprioritized req. Trigger: repost, funding announcement, or design-leadership hire signal.
• Salesforce: Watch → Ignore-until-trigger. Day 45. Director, Experience Design — Keynote Demos is a strategic demo/storytelling role, not product-design ownership. The posting language confirms it: "keynote demos, executive presentations, and customer engagements." Even fresh, the role rubric scores low on craft depth and AI exposure. Trigger: a product-facing Director+ or Staff IC design posting appears.
• Rubrik: Conflicted → Ignore-until-trigger. Role is open on Rubrik's site with strong language (Agent Cloud, 60-person design team, $1.57B ARR). But Khosla's board carries a February 6 original date, and The Ladders' "Reposted 1 week ago" label is a Grade C source that doesn't reset the clock. No primary-source revision evidence. Trigger: confirmed repost with new language, or hiring-manager signal.
• Atlassian: Watch → Ignore-until-trigger. Principal Product Designer, AI Mobile is live but exposes no posting date. The only date evidence is an aggregator listing from August 2025. Nearly a year old. Trigger: fresh posting date from primary source, or hiring-manager post from the AI Design team.
• Gusto: Ignore confirmed. Senior Product Design Manager Payroll posting dates to January 2026. Deeply stale. Trigger: new design leadership posting.

Accountability-Native — You Own the Trust Surface

Four roles. In each one, the design leader is directly accountable for the moment a human decides whether to trust, act on, override, or reverse what the AI produced. The trust calibration surface IS the product. Every evaluation across these four will test the same thing: can you design the surface where a human commits to an AI-generated output, knowing the cost of misplaced trust?

1. Amplitude. Product analytics platform whose AI layer (Wave, Session Replay AI, Guides) surfaces agent-generated product signals that teams decide whether to act on.

• Stage / Size / Capital: Public (AMPL), ~700 employees, $336M total raised pre-IPO. Post-IPO with AI-driven growth pivot.
• Offices: San Francisco (HQ), remote-friendly.
• Value chain: Midstream analytics platform sitting between raw product data and product-team decisions. Value concentrates in the interpretation layer: turning behavioral data into actionable signals. Design scope covers the full surface where data becomes decision.
• Best at: Tighter analytics-to-action pipeline than Mixpanel or Heap. Wave Opportunities pushes toward automated insight delivery rather than manual query building. The gap between "here's your data" and "here's what to do" is where Amplitude is placing its bet.
• Signals: Head of Product Design posting, day 17 (inferred from Issue #4 coverage at day 12 on July 19). New CPO buyer (in seat ~101 days; appointed April 14, 2026). The CPO-to-posting gap aligns with Statsig acquisition and Wave launches. Hot band. Note: ATS URL not re-verified this cycle; posting confirmed live as of Issue #4 (July 19). Verify direct posting URL before outreach.
• Tier: Growth-stage platform (post-IPO but behaving growth-stage: new CPO, AI pivot, function-building mandate). Tier Playbook: Read Growth-stage platform playbook before outreach.
• Rubric scores: Company 11/12 — AI centrality 3 (Wave/agents becoming core), Stage/equity 2 (public, limited equity upside), Design influence 3 (reports to new CPO, shapes function), Trajectory 3 (AI pivot accelerating). Role 14/15 — Total comp 2 (public company, cash-heavy), Scope 3 (reports to CPO, defines design function), Craft depth 3 (IC+manager hybrid explicit in posting), AI exposure 3 (Wave Opportunities, agent-surfaced insights), Portfolio value 3 (publishable trust calibration case).
• Decision-rights density: Accountability-native. You own the UX of Wave Opportunities: how agent-surfaced product signals are presented, how confidence is communicated, how teams decide to act or dismiss. The commitment point is when a product team looks at a Wave Opportunity and decides whether to change their roadmap based on what the agent found. That decision surface is yours. You influence which signals the AI surfaces and prioritization logic. You do not own platform infrastructure, pricing, or data pipeline architecture.
• Portfolio match: Amplitude's hardest design problem right now is the Wave Opportunity card. An AI agent tells a product team "your users are doing X and you should respond with Y." The team has to trust the signal enough to act. Your Brand Pulse case at Agentic Labs solves the same structural problem: an agent surfaces real-time brand sentiment signals, and a marketing team decides whether the signal is reliable enough to change strategy. The difference between a signal that gets acted on and one that gets ignored is trust calibration design: confidence indicators, source attribution, historical accuracy. The +20% daily transactions metric from Alibaba proves you can move behavior at platform scale when the trust design is right. The non-obvious edge: you've built agent traces and attribution systems in production at TinyFish, so you can speak to the engineering constraints of trust calibration, not just the design patterns. Most candidates can sketch the trust surface. You've shipped the plumbing underneath it.
• Verdict: Act. Company 11 ≥ 10, role 14 ≥ 12, day 17 in hot band. Contingent on ATS URL re-verification.

2. Vanta. Trust management platform that automates compliance certification (SOC 2, ISO 27001, HIPAA) through continuous monitoring and AI-powered evidence collection.

• Stage / Size / Capital: Series C ($150M, 2024), ~$100M+ ARR reported, ~500 employees. Pre-IPO with strong equity window.
• Offices: Remote-first, US. SF presence.
• Value chain: Midstream platform between compliance frameworks and the companies that need to prove adherence. Value concentrates in automating the trust verification process: replacing manual audits with continuous automated evidence collection. Design scope is the surface where automated compliance findings become actionable decisions.
• Best at: Speed to certification. The automation layer is the product, not a feature bolted onto consulting. Competitors sell audit prep. Vanta sells continuous proof.
• Signals: Head of Design posting, day ~18 (inferred from Issue #4 coverage at day 13 on July 19). VP-level, $365K–$513K posted comp range. Succession question unresolved: Deb Kawamoto still listed as VP of Design on the design careers page alongside Director of Design Braden Kowitz. Hot band, but the Kawamoto listing creates ambiguity about whether this is a replacement, an elevation, or a parallel hire. Note: ATS URL not re-verified this cycle; posting confirmed live as of Issue #4 (July 19). Verify direct posting URL before outreach.
• Tier: Growth-stage platform (Series C, accelerating, design org building). Tier Playbook: Read Growth-stage platform playbook before outreach.
• Rubric scores: Company 11/12 — AI centrality 2 (AI powers compliance automation, but the product is trust management), Stage/equity 3 (pre-IPO Series C, strong valuation, meaningful equity), Design influence 3 (VP-level hire, design has organizational weight), Trajectory 3 (accelerating in compliance automation market). Role 13/15 — Total comp 3 ($365K–$513K), Scope 2 (VP-level, but succession question creates scope uncertainty), Craft depth 2 (VP is strategic-weighted), AI exposure 3 (compliance automation trust surfaces), Portfolio value 3 (publishable trust design case).
• Decision-rights density: Accountability-native. You own how customers experience automated compliance findings: the surface where a customer decides whether to trust the automated evidence or manually re-verify. You own the design language for continuous monitoring dashboards, alert severity, and remediation guidance. The commitment point: a customer looks at Vanta's automated finding and decides to submit it to their auditor without manual review. That's the moment. You influence compliance framework logic and automation scope decisions. You do not own security architecture, certification standards, or audit methodology.
• Portfolio match: Vanta's hardest design problem is false-positive fatigue in compliance automation. If customers verify every automated finding, the automation fails its value proposition. If they trust everything blindly, compliance fails. Your Thermo Fisher mySupply case solved the same structural problem in pharma supply chain: a new system that users had to trust for high-stakes decisions where the cost of error was regulatory and financial. 100% adoption across 6 pharma partners proves you can design trust that sticks in regulated environments. The non-obvious edge: your TinyFish work on enterprise agent governance, specifically auditability, attribution, and reversibility, maps directly to the compliance automation trust surface. You've built the governance layer that makes automated findings trustworthy, not just the interface that displays them. You can explain why the dashboard needs to show provenance, not just draw the dashboard. That combination is thin in the market.
• Verdict: Act. Company 11 ≥ 10, role 13 ≥ 12, day ~18 in hot band. The Kawamoto succession question is a risk to investigate, not a reason to wait. Contingent on ATS URL re-verification.

3. HackerOne. Security platform combining AI agents (Hai) with human security researchers to discover, validate, and prioritize vulnerabilities across code, cloud, and AI systems.

• Stage / Size / Capital: Series E ($49M, January 2022), $160M total raised, 201–500 employees. No subsequent funding in 4.5 years. Likely profitable or approaching profitability. Equity window uncertain.
• Offices: San Francisco (HQ). Role location not confirmed from posting. Verify geographic eligibility before investing prep time.
• Value chain: Midstream platform between security researchers/AI agents and enterprise security teams. Value concentrates in the vulnerability discovery and validation layer. Design scope is the evaluation surface where AI-generated vulnerability reports become prioritized security actions.
• Best at: Largest bug bounty platform with the deepest researcher network. The Hai AI agent (90% customer adoption, 210% increase in AI vulnerability reports) is shifting the model from researcher-dependent to agent-augmented discovery. Nobody else has this combination of human network and AI capability in security.
• Signals: Director, AI Product Design live on Ashby. Reports to CPO Nidhi Aggarwal (appointed June 2025, in seat ~13 months). Aggarwal's appointment press release emphasized "AI-centric platform vision" and unifying the portfolio around "an integrated AI-powered experience that scales human security expertise through AI agents." Posting date not extracted from primary source. New signal, not previously tracked.
• Tier: AI-native (Hai AI agent is core product capability, 90% adoption, AI vulnerability discovery is the growth vector). Tier Playbook: Read AI-native playbook before outreach.
• Rubric scores: Company 9/12 — AI centrality 3 (Hai agent is core, 90% adoption), Stage/equity 2 (Series E, 4.5 years since last round, equity window unclear), Design influence 2 (Director level, reports to CPO, design team size unknown), Trajectory 2 (AI adoption metrics strong but no clear acceleration signal from funding or headcount). Role 13/15 — Total comp 2 (Director at ~300–500 person company), Scope 2 (Director within AI product vertical), Craft depth 3 (hands-on AI product design), AI exposure 3 (agent trust surfaces, vulnerability validation, AI red teaming), Portfolio value 3 (security trust design is highly publishable and differentiated).
• Decision-rights density: Accountability-native. You own how security teams evaluate AI-generated vulnerability reports: severity presentation, confidence indicators, false-positive handling. You own the Hai agent interaction surface where a security analyst decides whether an AI-found vulnerability is real, how severe it is, and whether to prioritize remediation. The commitment point: a security team looks at a Hai-generated vulnerability report and decides whether to allocate engineering resources to fix it. A false positive wastes expensive engineering time. A missed true positive is a breach. That calibration surface is yours. You influence agent capability scope and vulnerability prioritization logic. You do not own platform security architecture or researcher community management.
• Portfolio match: HackerOne's hardest design problem is agent attribution in vulnerability reports. When Hai finds a vulnerability, the security team needs to know: how was it found, how confident is the finding, is this a false positive? Your Carrier IQ case at Agentic Labs solves the same pattern: an agent automates insurance quote processing, and a human underwriter must validate the output before committing. The design decision was how to present agent confidence levels so underwriters could triage efficiently without re-doing the agent's work. Your TinyFish production work on agent traces and auditability is the direct technical analog. The non-obvious edge: most AI design candidates can design trust surfaces. Very few have built agent trace infrastructure in production AND designed the user-facing trust calibration layer. You've done both sides of that wall.
• Verdict: Watch. Company 9 ≥ 8, role 13 ≥ 10. Clears Watch on both dimensions. Company score (9) falls short of Act threshold (10+). Posting date unverified from primary source. Geographic eligibility unconfirmed. Two unknowns to resolve before upgrading.

4. Brex. AI-first financial platform for corporate spend management, with AI agents handling categorization, policy enforcement, and financial recommendations.

• Stage / Size / Capital: Pre-IPO, ~$1.2B total raised, valued at $12.3B (2022), ~1,100 employees. Strong equity window.
• Offices: San Francisco (HQ), remote-friendly.
• Value chain: Downstream end-user application. Corporate financial services used directly by finance teams and employees. Value concentrates in the financial workflow layer where AI reduces manual work and enforces policy. Design scope is the decision surface where AI recommendations become financial actions with real dollar consequences.
• Best at: AI-native financial workflows for startups and mid-market. The AI layer isn't bolted on. It's how the product works. That means the design leader isn't retrofitting trust onto an existing product. The trust surface was designed in.
• Signals: Staff Product Designer, AI posted July 13 (datePosted from direct company page), day 11. Hot band. Below Director level but strong rubric match at a company scoring 12/12 on the company rubric.
• Tier: AI-native (AI is central to product differentiation, role is specifically AI-focused). Tier Playbook: Read AI-native playbook before outreach.
• Rubric scores: Company 12/12 — AI centrality 3 (AI agents core to product differentiation), Stage/equity 3 (pre-IPO, $12.3B valuation, meaningful equity), Design influence 3 (strong design culture, design leadership at the product table), Trajectory 3 (accelerating, AI-first positioning). Role 13/15 — Total comp 2 (Staff IC level), Scope 2 (IC, feature/product-level scope, not org-level), Craft depth 3 (hands-on AI product design, deep craft), AI exposure 3 (AI financial decisioning, policy enforcement, categorization), Portfolio value 3 (AI financial trust design, highly publishable).
• Decision-rights density: Accountability-native at the product surface level. You own specific AI product surfaces: how expense categorization, policy enforcement, and financial recommendations are presented to users. You own the interaction design for AI-generated financial decisions where errors have direct dollar consequences. Staff IC means "owns" is craft ownership of the design surface, not organizational authority over the design function. The commitment point: a finance team member sees an AI-categorized expense or AI-enforced policy decision and decides whether to accept, override, or escalate. You influence AI model behavior at the feature level and product strategy for the AI vertical. You do not own broader platform decisions or org-level design strategy.
• Portfolio match: Brex's hardest design problem is reversibility in AI financial decisions. When the AI categorizes an expense wrong or flags a legitimate transaction, the user needs to understand why, fix it without friction, and not lose trust in the system. Your Alibaba.com case is the closest analog: a $50B+ GMV platform where every interaction is a financial decision, and the +20% daily transactions metric proves you can design trust that moves financial behavior at scale. The +2.2pt NPS lift proves the trust wasn't just functional. Users felt it. The non-obvious edge: at Staff IC level, the evaluator is testing whether you've worked in the medium, not managed around it. Your TinyFish production work, shipping 3 products in 3 months with daily agent traces, proves you build AI products. That's the "built" vs. "designed" verb-level screen this role will apply. Show up as a builder.
• Verdict: Act. Company 12 ≥ 10, role 13 ≥ 12, day 11 in hot band. Staff IC level is below Director, but the rubric clears and the AI exposure quality is among the highest in this cycle.

Org-Transformation — You Change How Design Works With AI

Two roles where the design leader doesn't own a specific product trust surface. The mandate is to transform how the design function operates with AI across a large organization. The evaluation pattern shifts accordingly. These buyers are testing institutional change capacity, not agent-trace fluency. Enterprise political survival matters more here than production deployment stories.

5. Capital One. Consumer and commercial banking platform with one of the largest technology organizations in financial services, ~55,000 employees, engineering-led culture, significant AI/ML investment.

• Stage / Size / Capital: Public (COF), ~55,000 employees, $36B+ revenue. No meaningful equity upside.
• Offices: McLean, VA (HQ), Richmond, New York, San Francisco, Plano. Geographic flag: SF is listed as an office, but the specific role's geographic eligibility for SF-based candidates is not confirmed. Verify before investing prep time.
• Value chain: Vertically integrated financial services. Consumer banking, credit cards, auto lending, commercial banking. Value concentrates in the technology layer that differentiates Capital One from traditional banks. Design scope is org-wide transformation of how the XD (Experience Design) function integrates AI into design practice.
• Best at: Genuine engineering culture inside a major bank. One of the few financial institutions where technology leadership drives product decisions rather than following them. That's rare enough to be worth naming.
• Signals: Director, Product Design — AI in Experience Design posting refreshed July 20. Day 4 from refresh. The Battlecard analysis framed this as a second-slate window: the first search failed, and the revised posting reveals what the committee actually learned they needed. The mandate is AI governance across the XD organization. Note: ATS URL not re-verified this cycle; posting confirmed refreshed as of Issue #4 Battlecard analysis (July 20). Verify direct posting URL before outreach.
• Tier: Enterprise platform (large org, mature evaluation process, matrix environment). Tier Playbook: Read Enterprise platform playbook before outreach.
• Rubric scores: Company 7/12 — AI centrality 2 (AI is a strategic bet, not core product), Stage/equity 1 (public large-cap, no meaningful equity), Design influence 2 (Director in large org, influence through matrix), Trajectory 2 (stable, not accelerating). Role 11/15 — Total comp 2 (Director at large bank, strong cash, no equity), Scope 2 (org-transformation mandate but within enterprise constraints), Craft depth 2 (strategic and governance-weighted, less hands-on), AI exposure 3 (AI governance across entire XD function), Portfolio value 2 (org-transformation case, less publishable than product design).
• Decision-rights density: AI-adjacent, org-transformation variant. You own how the XD organization adopts AI tools and practices: the operating model, governance frameworks, quality standards for AI-assisted design work. You own the definition of what "good" looks like when designers use AI in their workflow. You do not own a product trust surface. You own the process by which hundreds of designers change how they work. You influence AI product features through design governance and standards. You do not own product decisions made by PM/engineering that design supports.
• Portfolio match: Capital One's hardest design problem is institutional adoption at scale. How do you get hundreds of designers across a 55,000-person organization to change their workflow? Your Alibaba.com case is the direct analog: you named a structural gap in the B2B platform, built the mandate for change, and led 3 cross-functional sprints across homepage, search, and PDP. That's org transformation at enterprise scale. Identifying the problem, building consensus, leading the change. The non-obvious edge: most candidates for an AI-in-XD governance role are either enterprise design leaders who haven't built AI, or AI practitioners who haven't operated at enterprise org scale. You have both. Alibaba for the enterprise transformation. TinyFish for the hands-on AI production experience. You can write the governance framework AND explain why it needs to account for agent hallucination rates. That combination is thin in the market.
• Verdict: Watch. Company 7 < 8, but role 11 ≥ 10, clearing Watch on the role dimension. Refreshed July 20 (day 4) resets the decay clock. The company rubric score is the lowest in this cycle's Watch set. Limited equity, limited trajectory acceleration. The role's value is AI governance experience and portfolio diversification, not financial upside.

6. Adobe. Creative and marketing technology platform whose GenStudio product combines generative AI models (Firefly), agents, and automation workflows for enterprise creative production at scale.

• Stage / Size / Capital: Public (ADBE), ~30,000 employees, $21B+ revenue. No meaningful equity upside at current valuation.
• Offices: San Francisco, San Jose (HQ). Role is SF or SJ.
• Value chain: Upstream creative infrastructure. The dominant platform for professional creative workflows. GenStudio extends this into AI-powered enterprise content production. Value concentrates in the creative tooling layer where AI-generated content becomes production-ready assets. Design scope is the creative trust surface where enterprise marketing teams decide which AI outputs to use, edit, or reject at production scale.
• Best at: Creative production tooling. No competitor has Adobe's depth across the creative workflow from ideation to distribution. GenStudio is the bet that AI agents can automate the production layer while maintaining creative quality. Whether that bet pays off is a design problem.
• Signals: Director of Product Design, GenStudio is live. Workday extract says "Posted 30+ Days Ago" but does not expose an exact date. Cannot place precisely in the 22–42 Watch band or 43+ stale band. Role description: "define the future of creativity and scaled production workflows for enterprise customers by combining generative AI models and agents, automation workflows, and Adobe marketing and creative tools."
• Tier: Enterprise platform (large org, mature design culture, established evaluation process). Tier Playbook: Read Enterprise platform playbook before outreach.
• Rubric scores: Company 8/12 — AI centrality 2 (Firefly/GenStudio is a major bet but Adobe is broader), Stage/equity 1 (public large-cap, no meaningful equity), Design influence 3 (Adobe has the strongest design culture of any enterprise tech company), Trajectory 2 (stable/growing, GenStudio is the acceleration vector). Role 13/15 — Total comp 2 (Director at Adobe, strong cash, no equity), Scope 2 (GenStudio is a product line within Adobe), Craft depth 3 (agentic creative workflows, hands-on design leadership), AI exposure 3 (generative AI + agents in creative production), Portfolio value 3 (agentic creative production workflows, highly publishable and differentiated).
• Decision-rights density: Org-transformation with accountability-native elements. You own design vision for GenStudio's agentic creative workflows: how enterprise customers experience AI-generated content production, how they decide which outputs to trust, edit, or reject. You own the creative quality bar for AI-generated assets at production scale. This role sits between org-transformation and accountability-native. You change how creative production works with AI (transformation), and you also own the trust surface where enterprise customers evaluate AI-generated creative (accountability). The commitment point: a marketing team looks at a batch of AI-generated content variations and decides which ones are production-ready without human redesign. You influence Firefly model capabilities and enterprise workflow architecture. You do not own Adobe's broader AI strategy or Firefly model training priorities.
• Portfolio match: Adobe's hardest design problem is creative trust at production scale. When GenStudio generates 50 content variations for an enterprise campaign, a marketing team needs to evaluate quality, brand consistency, and production-readiness without reviewing each one manually. Your Equinox+ case is the closest analog: 0→MVP in 3 months across 5 brands, where the design challenge was maintaining creative quality and brand consistency across multiple brand identities at speed. The 4.8★ rating proves the creative quality held under velocity pressure. Your Agentic Labs work (Brand Pulse, Retail Velocity) adds the AI layer: you've designed surfaces where AI-generated signals are evaluated by humans for quality and actionability. The non-obvious edge: most candidates for this role come from either creative production (and lack AI agent experience) or AI product design (and lack multi-brand creative production experience). GenStudio sits at the intersection. You've worked both sides.
• Verdict: Watch. Company 8 ≥ 8, role 13 ≥ 10. Clears Watch on both dimensions. Posting age is the risk. "30+ Days Ago" could mean this is already past the stale threshold. If a precise date surfaces and it's within the 22–42 band, reassess for Act. The role's AI exposure quality and portfolio value are among the highest this cycle.

Clinical Trust — The Stakes Are Medical

One role where the trust surface carries clinical consequences. Maven stands alone because the evaluation pattern is fundamentally different from everything above. The Healthcare/Regulated tier tests trust design where a wrong answer means a patient acts on incomplete guidance, delays care, or follows a recommendation that should have been escalated to a provider. The buyer here isn't testing your agent-trace fluency. They're testing whether you understand what trust costs when the user is vulnerable and the margin for error is biological.

7. Maven Clinic. Virtual clinic for women's and family health covering fertility, pregnancy, postpartum, pediatrics, and menopause, serving enterprise employers and health plans.

• Stage / Size / Capital: Series E ($125M, 2024), ~600 employees, valued at $1.7B. Pre-IPO with strong equity window.
• Offices: New York (HQ). Role is NYC. Geographic flag: NYC location requires geographic flexibility confirmation.
• Value chain: Downstream end-user application. Virtual healthcare delivered directly to patients through employer-sponsored plans. Value concentrates in the clinical care delivery layer where digital interactions replace or augment in-person visits. Design scope is the clinical trust surface where patients make health decisions based on digital interactions with providers and content.
• Best at: Virtual women's and family healthcare at scale. The largest telehealth platform focused on fertility, pregnancy, and pediatrics, with clinical outcomes that justify employer investment. Nobody else owns this vertical with this depth.
• Signals: VP of Design live on Greenhouse. First published June 16, updated July 8. Day 38. Reports to CPO. Leads ~15 designers across product design, brand design, and user research. The July 8 update, 22 days after first publish, suggests the committee learned something and revised. Extended band, expiring July 29.
• Tier: Healthcare/Regulated (regulated domain, high-stakes user decisions, clinical consequences shape the design mandate). Tier Playbook: Read Healthcare/Regulated playbook before outreach.
• Rubric scores: Company 11/12 — AI centrality 2 (AI in healthcare is emerging, not yet core product), Stage/equity 3 (pre-IPO Series E, $1.7B valuation, meaningful equity), Design influence 3 (VP of Design reporting to CPO, leads 15-person team), Trajectory 3 (accelerating, women's health market expanding, enterprise adoption growing). Role 13/15 — Total comp 3 (VP level at well-funded late-stage startup), Scope 3 (VP, builds and leads function, reports to CPO), Craft depth 2 (VP is strategic-weighted, less hands-on), AI exposure 2 (healthcare AI is emerging, trust design is the real surface), Portfolio value 3 (healthcare trust design, highly publishable and differentiated from typical AI portfolio).
• Decision-rights density: Accountability-native, clinical variant. You own patient-facing design surfaces: how clinical recommendations are presented, how care plans are communicated, how patients navigate between digital and in-person care. You own trust design for telehealth interactions where a patient decides whether the digital experience is sufficient or whether they need to seek in-person care. You own brand design and research strategy across the function. The commitment point: a patient, often pregnant, often anxious, often making decisions for someone who can't advocate for themselves, looks at Maven's interface and decides whether to trust the guidance enough to act on it. Highest-stakes trust surface in this cycle. You influence clinical workflow design and provider-facing tools. You do not own clinical protocol decisions, regulatory compliance requirements, or medical content standards.
• Portfolio match: Maven's hardest design problem is trust design for vulnerable users making health decisions under uncertainty. A pregnant patient evaluating fertility treatment options or a new parent assessing pediatric symptoms needs to trust the digital experience enough to follow the guidance, or know when to escalate to in-person care. Your American Red Cross case is the direct analog: $847K disbursed to disaster survivors, 6 systems consolidated to 1, national deployment. The users were in crisis, vulnerable, making high-stakes decisions, and the system had to earn trust immediately with no second chance. The non-obvious edge: most healthcare design candidates have clinical workflow experience but lack your trust framework depth. Your "Trust Is the New Interface" essay and the Watch→Verify→Delegate trust ladder give you a published, citable framework for how trust is built incrementally. In healthcare, patients don't delegate to the system on day one. They watch, then verify, then gradually trust. You've named that progression and designed for it. The clinical context is new. The trust architecture is yours.
• Verdict: Watch (decay-limited from Act). Company 11 ≥ 10, role 13 ≥ 12. Rubric scores clear Act. But day 38 places the posting in the 22–42 extended band, capping the verdict at Watch per decay rules. The July 8 update is a positive signal: active search, not stale. Expires July 29.