About Us

We’re an early-stage B2B SaaS company building AI-powered products for enterprise teams. Our customers care deeply about safety, trust, compliance, and revenue impact, and we help them launch AI features confidently.

We’re looking for a Product Manager who can own customer-facing dashboards end-to-end, defining the right KPIs, partnering with eng/ML on the data and evaluation, and turning complex signals into simple, decision-ready UI for our customers.

What You’ll Do

Own Metrics & Evaluation Frameworks

• Define and evolve the core Metrics framework for AI readiness, safety, security, and compliance.

• Partner with ML/eng to design metrics and evaluation loops that reflect real-world risk.

• Work with GTM and customers to ensure metrics map cleanly to business outcomes and internal reporting needs.

Build Customer Dashboards & UX

• Own the end-to-end UX for customer-facing dashboards: readiness scores, risk heatmaps, experiment views, trend charts, and drill-downs into specific conversations.

• Translate complex technical signals (attack success rates, severities, false positives/negatives) into simple visualizations and narratives.

• Work closely with design to define information architecture, layouts, and interaction patterns.

Drive Customer Discovery & Feedback Loops

• Run customer discovery with T&S leaders, PMs, compliance, and security stakeholders to understand how they make decisions and what data they trust.

• Turn feedback and usage data into a prioritized roadmap for dashboards, reports, and self-serve analytics.
  

Experimentation & Data-Driven Decisions

• Define success metrics and guardrail metrics for new features; partner with eng/ML on rollouts.

• Use product analytics and customer data to understand adoption, engagement, and impact of dashboards and reports.

What We’re Looking For

Must-Haves

• 4–8+ years of Product Management experience in B2B SaaS and/or AI/ML-powered products.

• Demonstrated experience owning customer-facing dashboards, analytics products, or reporting surfaces.

• High comfort with metrics: defining KPIs, reasoning about tradeoffs, and turning raw data into clear definitions and thresholds.

• Strong collaboration with engineering, ops, and data/ML teams

• Excellent written and verbal communication; you can explain complex AI risk concepts in simple language to non-technical stakeholders.

Nice-to-Haves

• Experience with LLMs, GenAI, or ML-based products

• Background working with trust & safety, security, compliance, or risk teams.

• Prior startup experience building v1 products and iterating quickly with design partners.