Product Manager, AI Enablement - Pindrop

Who We Are

Pindrop is a cybersecurity company delivering continuous identity verification across voice, video, and digital interactions. Powered by the Pindrop Intelligence Network, our unified trust platform helps enterprises answer three critical questions:

• Is this identity synthetic? Detect AI-generated and manipulated voice and video.
• Is this identity risky? Identify fraud, suspicious behavior, and other risk signals.
• Is this identity known? Authenticate identities using voice, device, behavioral, and other signals.

Pindrop brings this intelligence together across purpose-built trust engines to help organizations make informed decisions in real time — protecting customers, workforce interactions, and emerging AI-driven interactions. Pindrop protects billions of high-risk interactions for enterprises, including leading financial institutions, insurers, and healthcare organizations.

What You’ll Do

As Product Manager, AI Enablement, you will own the product experience that turns Pindrop's AI capabilities into practical, repeatable workflows across Finance, RevOps, People, Recruiting, Customer Success, Marketing, and Legal. You are a product manager whose users happen to be internal—mapping processes, identifying high-value problems, building agentic solutions, and proving ROI.

You'll sit within the centralized AI Operations group and work alongside an existing AI practitioner already embedded in Product. The first 50% of your time will focus on Finance and RevOps, with the rest targeting where the highest-value problems emerge.

Key Responsibilities

• Map processes first: Document current-state workflows end to end with stakeholders, extracting context and tools to identify precisely where AI reduces friction.
• Architect and deploy tools: Build a portfolio of internal AI tools, agentic workflows, and custom integrations (via APIs and automation frameworks) that push past prototypes into stable, production-ready states.
• Audit existing stacks: Audit built-in AI capabilities in existing tools before standing up new software.
• Extend core infrastructure: Extend Pindrop's shared AI platform and agent infrastructure to new use cases rather than creating parallel stacks.
• Operationalize AI lifecycles: Design and run operating processes moving AI concepts from prototype to production (evals, human-in-the-loop review, rollout criteria, and monitoring).
• Consolidate functional learnings: Standardize cross-functional tooling and documentation to ensure reuse across departments.
• Demonstrate ROI: Define success metrics, instrument usage, track adoption, and clearly quantify returns on AI investments.
• Drive adoption: Facilitate practical enablement through workshops, onboarding paths, office hours, and coaching.

Who You Are

• A technical builder and product thinker: You go beyond prompt engineering with functional, shipped code.
• A process mapper first: You deeply analyze workflows before automating them.
• Frontier-fluent: You stay close to edge AI tooling and prioritize genuine utility over AI hype.
• A translator and teacher: You build cross-functional trust and turn skeptics into confident users.
• Rigorously skeptical: You stress-test model outputs, understand failure points, and rely on rigorous evaluation metrics.

Requirements

• Process mapping: Proven ability to document end-to-end workflows and build foundation-level solutions.
• Product experience: 3+ years in product management, product operations, AI enablement, or a related role owning scope, requirements, delivery, and metrics.
• Technical execution: Direct experience scripting (e.g., Python), integrating APIs, and deploying production agentic systems.
• Tooling fluency: Hands-on fluency with frontier AI CLIs and coding environments (e.g., Claude Code, Codex, Cursor).
• Portfolio: A GitHub profile showing 3–4 built repositories or equivalent technical architecture proof.
• System design: Fluency in context engineering and ontology design.
• Evals & TDD: Strong conceptual command of test-driven and eval-driven development for AI systems.
• ROI measurement: Proven track record defining metrics and tracking user adoption.

Nice-to-Haves

• Experience with multi-agent orchestration frameworks and meta harnesses.
• Working knowledge of Model Context Protocol (MCP) or similar orchestration layers.
• Familiarity with RAG architectures, output evaluation, hallucination mitigation, and enterprise release gating.
• Exposure to AI acceptable-use policies and workforce governance frameworks.
• Experience in high-growth, AI-native B2B SaaS companies.

Travel

This US-based remote position requires at least 6 trips per year for team on-sites, cross-functional workshops, and company gatherings.