Senior AI Product Manager, Cybersecurity

Responsibilities

• Own the roadmap and strategy for the cybersecurity portfolio across training data, reinforcement-learning environments, agentic task suites, and evaluation products.
• Define the capability map for vulnerability discovery, proof-of-concept reproduction, patch generation and regression safety, secure code review, supply-chain analysis, malware and binary analysis, detection engineering, and incident triage.
• Determine which capabilities to build training data for, measure only, or decline across the offense–defense spectrum.
• Partner with machine-learning researchers and security practitioners on task specifications, grader design, and verifiable rewards.
• Drive infrastructure for reproducible vulnerability images, fuzzing and build toolchains, sandboxed execution, network-segmented ranges, and automated verification.
• Build sourcing pipelines that scale beyond hand curation.
• Own responsible-development processes, including containment, coordinated disclosure, sensitive-artifact handling, and customer vetting.
• Establish governance for data quality, contamination prevention, license and IP hygiene, reproducibility, and release management.
• Recruit and steward a contributor network of vulnerability researchers, exploit developers, malware analysts, detection engineers, and incident responders.
• Own external partnerships with open-source benchmark collaborations, academic security groups, and enterprise data partners.
• Work with frontier labs and enterprise customers to identify model security weaknesses, translate findings into roadmap priorities, and partner with go-to-market teams on launches and thought leadership.

Requirements

• 5+ years in product management, technical program management, consulting, or customer-facing technical roles, or equivalent practitioner experience with a path toward product ownership.
• Professional cybersecurity experience in areas such as vulnerability research, fuzzing, crash triage, reproducer development, patch and root-cause analysis, exploit development, malware analysis, red teaming, detection engineering, or incident response.
• Software engineering depth sufficient to read unfamiliar code and reason about runtime behavior.
• Familiarity with model post-training and evaluation, agentic scaffolds, and container-based rollout infrastructure.
• Excellent stakeholder management and executive communication skills.
• Sound judgment on dual-use questions and commitment to defender-focused capability measurement.
• Entrepreneurial mindset, bias for action, and comfort in ambiguous environments.

Nice-to-haves

• Competitive CTF experience, published CVEs, a bug bounty record, or OSS-Fuzz contributions.
• Knowledge of AI-for-security evaluation benchmarks, including CyberGym, Cybench, CVE-Bench, BountyBench, and CyberSecEval.

Compensation and Benefits

• Base salary range: $205,600–$257,000 USD.
• Compensation may include equity and benefits; the final salary depends on work location, skills, experience, qualifications, interview performance, and education or training.
• Benefits include comprehensive health, dental, and vision coverage, retirement benefits, a learning and development stipend, generous PTO, and potentially a commuter stipend.