Product Manager, AI Enablement

About this role

Who We Are

Pindrop is the Real Human + Right Human® Identity Trust Platform for the AI era. As AI-driven fraud and deepfakes erode trust in digital communication, Pindrop delivers continuous identity verification and deepfake detection across voice, video, and digital interactions in real time.

Enterprises rely on Pindrop to secure billions of high-risk customer interactions each year, including top U.S. banks, as well as leading insurers and healthcare providers. Powered by models trained on more than 1.5 billion real-world interactions annually and protected by 300+ patents, Pindrop restores trust while reducing fraud, lowering operational costs, and improving customer experience.

Recognized by TIME as one of the Top 10 Most Influential Software Companies of 2026 and by Inc. for Best in Business for Innovation, Pindrop is backed by leading investors including Andreessen Horowitz, IVP, and CapitalG.

What you’ll do

Pindrop is an AI-native company — but outside of Product, Engineering, and Research, most teams are still figuring out how to use AI in ways that actually stick. This role fixes that.

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 not an AI trainer or a help desk. You are a product manager whose users happen to be internal — and your job is to map their processes, identify the highest-value problems, build or configure the right agentic solutions, and prove the 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; the rest goes where the highest-value problems are.

More specifically, you will:

• Map processes before touching AI. Sit with stakeholders, document current-state workflows end to end, extract the context and tools involved, and identify precisely where AI can reduce friction — not the other way around.

• Build and manage a portfolio of internal AI tools, agentic workflows, and prompt-based copilots that solve discrete, measurable problems — and push every project past prototype into production.

• Audit AI capabilities already built into tools Pindrop has purchased (e.g., Greenhouse, ERP) before standing up new solutions; use what we've paid for first.

• Extend Pindrop's shared AI platform and agent infrastructure to new use cases rather than creating parallel stacks.

• Design and implement the operating process that moves AI concepts from prototype to production, including evaluation, human-in-the-loop review, rollout criteria, and post-launch monitoring.

• Consolidate learnings across functions — what worked in Finance should be usable in People; what worked in People should be accessible to CS — and build the tooling and documentation that makes reuse real.

• Make the ROI case. Define success metrics, instrument usage, track adoption, and be able to explain clearly what the company is getting for its AI investment.

• Drive adoption through practical enablement — workshops, onboarding paths, office hours, and coaching — focused on giving teams the guardrails they need to use AI well, not just permissively.

Who you are

• A builder, not just a user. You've gone past prompting and into building — you have a GitHub (or equivalent) with projects that prove it. You've shipped agentic systems that solve real problems, and you know how different it feels when the last 20% actually works.

• A process mapper first. Before you touch a tool, you understand the workflow. You can sit with a stakeholder, draw out what actually happens today, and find the leverage point — because you know you can't automate a process you haven't mapped.

• Frontier-fluent and self-directed. You track what's happening at the edge of AI tooling because you're genuinely curious, not because it's required. You know the difference between AI theater and AI value, and you're allergic to the former.

• A translator and teacher. You can walk into a Finance or HR meeting without owning their domain and still build credibility — through preparation, curiosity, and practical results. You get energy from turning a skeptic into a confident user.

• Rigorously skeptical. You stress-test outputs, understand where models break down, and hold a high bar before recommending a workflow to a real team. You know what an eval is and why it matters.

Your skill-set

Must-haves:

• Process mapping. This is the number-one requirement. You must be able to document a current-state workflow end to end, extract the tools and context involved, and use that map as the foundation for any AI solution. No map, no AI advantage.

• 3+ years in product management, product operations, AI enablement, or a closely related role at a technology company — with PM-level ownership of scope, requirements, delivery, and metrics.

• Hands-on experience with frontier AI CLIs and coding tools, including Codex, Claude Code, Anti-Gravity, and/or SuperGrok. We will ask to see what you've built.

• Demonstrated ability to build agentic systems that solve a discrete, measurable problem — not a demo, a shipped solution. A GitHub profile showing 3–4 built projects is a strong signal; candidates without one will be asked to walk through their work in detail.

• Fluency in context engineering and ontology design — ability to speak to both concepts and apply them in practice.

• Conceptual command of test-driven development and eval-driven development for AI systems; must understand why evals matter and how to define them, even if you're not writing them by hand.

• Proven ability to define success metrics, track adoption, and make the ROI case for AI investments to a skeptical stakeholder.

Nice-to-haves:

• Experience with meta harnesses and multi-agent orchestration frameworks.

• Familiarity with RAG pattern

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