Principal Product Manager, AI Platform

**Circle** Principal Product Manager, AI Platform; $200K–$260K + equity; United States (Remote)

**About Company** Circle is a global financial technology company at the forefront of the new internet of money. Our infrastructure — including USDC, a blockchain-based dollar — powers payments, commerce, and financial applications worldwide. We help businesses, institutions, and developers leverage breakthrough blockchain technologies to drive global economic prosperity and digital inclusion.

**About the Role** Circle is hiring a Principal Product Manager to define and drive the AI platform vision — the internal and external AI-powered capabilities that let Circle move faster, safer, and at higher quality across compliance, ops, and product surfaces. This role sits within the Product org (Thomas Fitzgerald's group) and owns AI product strategy end-to-end: from LLM/agentic system design to compliance automation, from model routing and eval tooling to the guardrails that make AI safe to deploy inside a regulated financial platform. You'll partner deeply with Engineering, Legal, Compliance, Risk, and Ops to ship AI capabilities that are as trustworthy as they are useful.

**Responsibilities** - Define and own the multi-year AI platform product strategy and roadmap. - Ship AI-powered products that meaningfully improve compliance, investigation, ops efficiency, or customer-facing surfaces at Circle scale. - Partner with AI/ML engineering on LLM guardrails, model routing, prompt/eval frameworks, and agent orchestration. - Drive cross-functional alignment across Product, Engineering, Legal, Compliance, Risk, and Ops for AI safety, policy, and audit-readiness. - Own measurable outcomes — model quality, cost, latency, adoption, ops-efficiency gains — with clear ROI narratives. - Set the internal bar for AI product craft: prompt design, evals, human-in-the-loop workflows, telemetry, and postmortems.

**Required Skills** A. 8+ years of product management experience with meaningful time at Principal or equivalent scope. B. Track record shipping production AI/ML products at scale — LLM applications, agentic systems, ML platforms, or AI-driven automation. C. Deep technical fluency to partner credibly with AI/ML engineering — prompt design, RAG, evals, model routing, guardrails. D. Product judgment in regulated or compliance-heavy environments (fintech, payments, crypto, healthcare, or equivalent). E. Cross-functional leadership across engineering, design, legal/compliance, and business stakeholders. F. Experience owning outcomes tied to concrete business metrics (revenue, cost, risk reduction, ops efficiency).

**Bonus Skills** - Direct fintech, payments, or crypto/Web3 background (USDC, stablecoin infrastructure especially). - Compliance automation exposure — KYC/AML, sanctions/PEP screening, transaction monitoring, dispute workflows. - Founding PM or 0→1 platform builds where you owned strategy, execution, and adoption. - Familiarity with agentic frameworks (LangGraph, CrewAI, etc.) and modern AI dev tooling (Claude, Cursor, Codex). - Prior experience partnering with security, risk, and legal on AI policy.

**Logistical Info** - Location: United States, remote. - Compensation: $200,000 – $260,000 base + equity. - **Other:** Full benefits and equity refresh; US work authorization required by default; flat, collaborative organization with high feedback velocity and direct communication expectations.

**Ideal Background** - Strongest signal — AI/ML product leadership inside fintech / payments / crypto: Coinbase (AI Agent Cockpit, compliance automation, investigator tooling), Stripe (Radar Assistant, LLM guardrails, ML platform), Block/Cash App, Robinhood, PayPal AI, Adyen, Plaid. - Also strong — enterprise AI platform PM: BlackRock (Data & AI Platform Product Strategy), Salesforce Einstein/Agentforce, ServiceNow AI, Databricks, Snowflake Cortex, Hugging Face. - Also strong — AI-native scale-ups where the PM owned real product surface: Anthropic, OpenAI, Perplexity, Cohere, Character, Adept, Sierra. - Candidates should be able to point to a shipped AI system with measurable outcomes (agent adoption, cost reduction, model-quality lift, ops-efficiency gains) — not just "worked on AI" adjacency. - Historical calibration: Anoosha Patlolla (Coinbase, LangGraph multi-agent, $22.5M automation roadmap), Ningyuan Huang (Stripe GPT-integrated Radar), Mike Henry (BlackRock AI platform strategy) are the reference archetypes. - Priyanjali Vasisht flag: "limited AI platform experience but relevant fintech and compliance automation background from Coinbase" — this is the trade-off Thomas will consider on the margins, but AI depth is the strongest filter. - HM: Thomas Fitzgerald (Product). Reports up through the broader Product org that Tom (owns majority of Circle product) sits above.

**Green Flags** - Named production AI/agentic systems with quantified impact (users served, cost saved, investigations automated). - LLM guardrail / prompt-eval / model-routing ownership at scale. - Compliance automation via AI — KYC, sanctions, transaction monitoring, dispute triage. - Multi-team cross-functional leadership, especially spanning eng + legal/compliance. - Real usage of modern AI dev tooling (Claude Code, Cursor, Codex, agentic frameworks) in their own product workflows. - Meaningful tenure (multi-year) at an AI-product employer.

**Red Flags** - ML/AI researcher profile without product ownership — can describe models but not products shipped, adopted, or measured. - "AI PM" claim with no evidence of shipped systems or measurable outcomes. - No fintech / regulated-industry exposure — Circle's AI product strategy is inseparable from compliance and risk context. - Jumpy tenure pattern (repeated sub-2-year stints). - Prior application or interview at Circle within the last 12 months (recurring rejection reason across Circle roles). - Salary expectations materially above $260K base ceiling — historical Circle US roles are firm on caps.

**Interview Process** 1. Recruiter screen. 2. Hiring manager screen with Thomas Fitzgerald. 3. Product sense interview. 4. Product execution / AI technical depth interview. 5. Cross-functional panel with engineering + compliance / risk partners. 6. Leadership interview.