Product Manager, AI Governance & Compliance

Dynamo AI•San Francisco, CA

About The Position

Every AI application needs policies that define acceptable behavior, and guardrails built and tuned against those policies. Legal, risk and compliance teams hold much of the knowledge those policies depend on. They have no prior experience or mandate to write policy definitions for guardrails or to annotate data, and there is usually no process that brings them into guardrail creation and optimization. They are also rarely the teams that buy our products, which are usually purchased by security and AI platform leaders. As Product Manager for AI Governance & Compliance, you will design how these teams take part in policy writing, guardrail creation and guardrail optimization where no such process exists today. That means working out who needs to contribute, what they sign off on, and how their participation is secured through the economic buyer. It also means building the product workflows that make their contribution practical within the time they can give. This requires an understanding of how large enterprises assign responsibility and how teams outside the buying organization are brought into new work. You will also support work on audit-ready evidence records and on turning regional regulations into enforceable guardrails. Over time, your scope will expand to AI system inventory and risk tiering, and to the procurement, privacy and legal reviews enterprises run on AI vendors. You will present to customers' senior risk and legal stakeholders, build credibility as an authority on these problems, and lead customer engagements when needed.

Requirements

2 to 4 years of product management experience in GRC, regtech, legal tech, risk management or enterprise workflow software.
Experience working directly with legal, risk, compliance or audit teams at large enterprises, and an understanding of their mandates, incentives and how they make and document decisions.
A track record of bringing a team into a new process or responsibility it did not previously own, in an organization where that team did not report to you or to the project sponsor.
Understanding of enterprise decision-making: how budget holders, reviewers and approvers differ, and how to secure time and sign-off from teams outside the buying organization.
Working knowledge of how regulated enterprises govern models and AI, including model risk management practices such as SR 11-7 and frameworks such as the EU AI Act, NIST AI RMF and ISO/IEC 42001.
Enough technical understanding of LLMs, evaluations and guardrails to discuss precision, recall and false positive tradeoffs with engineers and explain them to non-technical reviewers.
Strong writing and presentation skills, with experience producing documents that hold up to review by legal and risk teams.
Willingness to spend 4 to 6 weeks per year at customer sites, and more during large contract engagements.

Nice To Haves

Prior experience in model risk validation, compliance, internal audit or risk consulting.
Experience with privacy (GDPR, DPIAs) or third-party risk management.
Experience designing annotation, labeling or review workflows for subject matter experts.
Legal training or experience interpreting regulation.
Experience in financial services or insurance.

Responsibilities

Own the roadmap for legal, risk and compliance workflows against defined customer problems and success metrics.
Map each customer's stakeholders: the economic buyer, the legal, risk and compliance teams whose input is needed, and the approvers. Secure those teams' participation through the buyer.
Design and run engagement models with customers, such as responsibility assignments and review cadences, and turn what works into product.
Lead engagements with model risk, compliance, legal and privacy teams, and present to senior risk and legal stakeholders.
Translate regulatory requirements and customers' internal policies into product requirements, working with engineering and research.
Work with the DynamoEval and DynamoGuard PMs on shared policies, evaluation evidence and reporting.
Track AI regulation and supervisory guidance, and assess what each change means for customers and the roadmap.