דרושים » מוצר » Senior Data Product Manager, AI

our company secures AI apps, agents and models from build to production. Enterprises are shipping AI faster than they can secure it, attack techniques evolve constantly, and every company draws the line on acceptable behavior in a different place. No enterprise can keep up with that on its own, and our company is how they do it. At the heart of the platform sit two engines: the attack engine finds the failures before attackers do, and the defense engine blocks them in production without breaking legitimate traffic.
We are hiring a Senior PM who will lead the strategy, vision, and execution of our core engines. This PM sits inside the DS workflow - and can read the notebooks, argue about the eval set, query the data themselves - and translate between model reality and customer reality in both directions.
This is not a PM role that writes a PRD and waits. It is a PM role where I pulled the data, heres what moved the F1, and heres what it means for customers is the expected level of answer.
What you'll own
Attack engine roadmap. Coverage and depth of adversarial techniques, novel-attack generation quality, agentic/multi-turn testing, zero-day and model-risk research productization. You decide which harm categories, modalities, and attack families we invest in next, and you defend that call with evidence.
Defense engine roadmap. Classifier quality across policies and languages, the precision/recall/latency triangle, customer-specific tuning, and the path from "a red team finding" to "a guardrail that blocks it." Closing that attack→defense loop is the single highest-leverage thing this role can do.
Evaluation as a product surface. Own how we define, measure, and publish model quality - benchmark datasets, eval harnesses, golden sets, regression gates, and the metrics customers actually see. When a customer asks "how accurate is this," the answer and the evidence behind it are yours.
Data strategy for the engines. What data we collect, label, retain, and feed back into training; how customer feedback (overrides, false-positive reports, session feedback) becomes a training signal; and how all of it stays within our customer data commitments
The DS↔Engineering seam. Model artifacts, schemas, and pipelines. You keep that contract explicit and get research into production.
Day to day
Run discovery with AI Product Owners, security engineers, and CISOs on what attacks they fear and what false positives they can't tolerate
Turn fuzzy customer harm into a testable engine requirement with a measurable bar
Prioritize the DS backlog with the DS lead; make the hard calls on coverage vs. depth vs. latency
Own the quality narrative for sales, solution engineering, and security reviews - backed by numbers you can defend
Partner with design and platform PMs so engine capability becomes a usable product, not a config flag
Track competitive and academic movement in adversarial ML and guardrails, and translate it into roadmap pressure.