Senior AI Product Manager (Cybersecurity)

TL;DR

Senior AI Product Manager (Cybersecurity): Building and owning a cybersecurity product portfolio for AI training data, reinforcement-learning environments, agentic task suites, and evaluation products with an accent on execution-grounded verification, responsible development, and scalable security infrastructure. Focus on defining capability standards, designing reproducible vulnerability and patch-validation workflows, and translating frontier-model security gaps into products for research labs and enterprise customers.

Location: Not specified.

Company

Scale AI develops data and full-stack technologies that help organizations train, evaluate, and deploy reliable AI systems.

What you will do

• Own the strategy and roadmap for cybersecurity training data, RL environments, agentic task suites, and evaluation products.
• Define capability taxonomies covering vulnerability discovery, exploit reproduction, patch validation, malware analysis, detection engineering, and incident triage.
• Partner with ML researchers and security practitioners on task specifications, grader design, verifiable rewards, and execution-based evaluation.
• Drive infrastructure for reproducible vulnerability images, fuzzing toolchains, sandboxed execution, network-segmented ranges, and automated verification.
• Establish governance for data quality, contamination prevention, licensing, reproducibility, responsible disclosure, and release management.
• Build practitioner, research, enterprise, and frontier-lab partnerships and convert customer needs into product launches.

Requirements

• Substantial hands-on cybersecurity experience in areas such as vulnerability research, fuzzing, exploit development, malware analysis, red teaming, detection engineering, or incident response.
• 5+ years in product management, technical program management, consulting, customer-facing technical roles, or equivalent practitioner depth.
• Understanding of AI-for-security evaluation benchmarks and the limitations of current evaluation methods.
• Software engineering depth sufficient to read unfamiliar code, reason about runtime behavior, and collaborate with senior engineers and ML researchers.
• Familiarity with model post-training, agentic scaffolds, and container-based rollout infrastructure.
• Strong stakeholder management, executive communication, judgment on dual-use risks, and comfort working in ambiguous environments.

Culture & Benefits

• Inclusive and equal-opportunity workplace focused on developing reliable AI systems.
• Eligible roles include base salary, equity, comprehensive health, dental and vision coverage, and retirement benefits.
• Learning and development stipend and generous paid time off.
• Additional benefits may include a commuter stipend.

Hiring process

• Compensation and potential equity eligibility are determined during the interview process based on location, skills, experience, qualifications, and interview performance.
• Candidates must wait 90 days before being reconsidered for the same role after an application decision.