Senior Data Product Manager (AI)

Location: Ramat Gan, Israel

Company

Builds AI trust, safety, and security products covering model evaluation, red-teaming, runtime guardrails, and drift detection.

What you will do

• Lead the roadmap for attack and defense engines, including adversarial techniques, novel-attack generation, agentic testing, classifiers, guardrails, and model-risk research.
• Own evaluation as a product surface, including benchmark datasets, evaluation harnesses, golden sets, regression gates, and customer-facing quality metrics.
• Define the data strategy for collection, labeling, retention, training feedback, overrides, false-positive reports, and session feedback.
• Translate customer harm and security needs into measurable engine requirements and prioritize the data science backlog.
• Work across data science, engineering, design, platform product, sales, solution engineering, and security stakeholders to move research into production.
• Track developments in adversarial machine learning and guardrails and convert them into roadmap decisions.

Requirements

• 5+ years of product management experience, including at least two machine-learning or data-science-backed products shipped to users.
• Experience with LLM-era concepts including evaluations, prompt engineering, RAG, agentic systems, and LLM-as-judge failure modes.
• Strong understanding of precision and recall tradeoffs, class imbalance, train/test leakage, annotation quality, and benchmark reliability.
• Experience defining and defending model quality metrics to customers or regulators.
• Track record of working closely with data scientists and research engineers.

Nice to have

• Security or adversarial machine learning background.
• Familiarity with data pipeline, feature, and labeling infrastructure.
• Experience with enterprise buyers in regulated industries.

Culture & Benefits

• Work on products safeguarding communicative technologies across AI platforms, enterprises, and user-generated-content platforms.
• Address AI security challenges across the full lifecycle, from pre-deployment evaluation to runtime protection and ongoing drift detection.
• Collaborate with data scientists, research engineers, security engineers, AI product owners, CISOs, design, and platform product teams.