What you'll own

Transaction Monitoring. The rules engine and ML model layer that generates alerts - scenario design, threshold calibration, typology coverage, backtesting against historical transaction data. You'll define what the system flags, why, and how those decisions evolve as customer behavior and fraud typologies shift.

KYC / KYB. Onboarding risk workflows: identity verification, document checks, beneficial ownership, ongoing monitoring triggers, and risk re-scoring. Orchestrate data sources dynamically based on risk tier.

Screening. Sanctions (OFAC, UN, EU), PEPs, adverse media, and negative news. Understand the matching problem and how to make screening defensible to an examiner without generating alert volumes that bury the ops team.

Case Management. The workflow layer: alert routing, case assignment, escalation logic, analyst productivity tooling, QA/QC review, disposition tracking. Design human-in-the-loop systems where humans add the most value and AI can carry the load.

SAR Filing & Regulatory Reporting. FinCEN SAR workflow end-to-end: narrative generation, supporting documentation, filing status, audit trail. Build product that holds up under regulatory scrutiny.

AML Data Strategy. The data model that powers all of the above: transaction graphs, entity resolution, behavioral baselines, consortium signals, model feature pipelines.

How you'll work

You are an AI-native PM. You use AI tools throughout your entire workflow — not as a novelty, but as a force multiplier.

For discovery: Use AI to synthesize call transcripts, cluster themes across interviews, scan regulatory guidance and examiner findings, and surface competitive product gaps faster than any manual process.
For building: Use AI to generate first-draft PRDs, stress-test your own specs, write and iterate on user stories, prototype UI flows, and pressure-test edge cases before engineering touches them.
For design: Use AI to explore multiple UX directions quickly and give the design team a sharper brief.
For analysis: Use AI to query product and operational data, interpret model outputs, and build the evidence base for roadmap decisions.

Requirements

Full-stack AML product depth. You've shipped product across at least two of the five areas above - in production, with real users, under real regulatory scrutiny.
AML strategy knowledge. You understand how AML programs are designed and calibrated, not just executed. You know the difference between a structuring typology and a layering typology and have an opinion on which detection approach fits each. You understand model drift, backtesting methodology, and what a model validation report covers.
Technical and data fluency. Your engineers trust your specs. You can read a data schema, write a query to validate a hypothesis, and spot when an ML model is being misapplied. You understand the explainability problem in AI-assisted compliance decisions.
Regulatory literacy. BSA, FinCEN, OFAC, FATF. You understand what examiners are looking for, what triggers an MRA, and how to build a defensible audit trail.
Bias toward speed. You use AI tools to move faster, test sooner, and learn earlier.

What success looks like

30 days: Run discovery with at least 5 AML/compliance buyers and produce a written synthesis. Review current AML product surface and identify the two biggest gaps.
60 days: Deliver one complete PRD ready to build. Have a clear point of view on the SAR narrative automation problem.
90 days: Lead calls with CCO prospects. Own a 12-month AML roadmap with clear prioritization rationale. Ship at least one thing to production.

Benefits

Competitive salary and equity packages, including a 401k plan
Remote-first culture — work from anywhere
100% Employer covered comprehensive health, dental, and vision insurance with a top tier plan for you and your dependents (US)
Unlimited PTO policy
AI First company; both Co-Founders are engineers at heart; and over 50% of the company is Engineering