About the job
We're looking for a Senior AI Product Manager to build and own Scale's Cybersecurity portfolio — the data, environments, and evaluations frontier labs use to train and measure security capability in their models. This is a build role: you will define the strategy and standards for a product line that does not exist yet.
Responsibilities
Own the roadmap and strategy for Scale's Cybersecurity portfolio across training data, RL environments, agentic task suites, and evaluation products — and stand the product line up end to end, from task taxonomy and sourcing through pricing and first external release.
Define the capability map we train and measure against: 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.
Make the strategic call on where Scale competes across the offense–defense spectrum — which capabilities we build training data for, which we only measure, and which we decline.
Partner with ML researchers and security practitioners on task specifications, grader design, and verifiable rewards, holding to execution-grounded verification wherever possible: a task counts as solved only when the reproducer fires or the patch holds without breaking functionality.
Drive the infrastructure roadmap — reproducible vulnerability images, fuzzing and build toolchains, sandboxed execution, network-segmented ranges, automated verification — and build sourcing pipelines that scale past hand-curation.
Own the responsible-development posture: containment, coordinated disclosure for live vulnerabilities surfaced during task construction, need-to-know handling of sensitive artifacts, and customer vetting, working with Security, Legal, and Policy to make these processes real rather than nominal.
Establish governance for data quality, contamination prevention, license and IP hygiene, reproducibility, and release management.
Recruit and steward a contributor network of working practitioners — vulnerability researchers, exploit developers, malware analysts, detection engineers, incident responders — and design quality controls that hold up when reviewers are validating work at the edge of their own expertise.
Own external partnerships across open-source benchmark collaborations, academic security groups, and enterprise data partnerships.
Work directly with frontier labs and enterprise customers to understand where their models fail on security work, translate that into roadmap, and partner with GTM on launches and thought leadership.
Qualifications
Minimum
Real cybersecurity work under your belt, rather than security-adjacent product experience: vulnerability research, fuzzing and crash triage, reproducer development, patch and root-cause analysis, exploit development, malware analysis, red teaming, detection engineering, or incident response. Competitive CTF, published CVEs, a bug bounty record, or OSS-Fuzz contributions all count.
5+ years in product management, technical program management, consulting, or customer-facing technical roles — or equivalent depth as a practitioner with a clear pull toward product ownership.
A working view of the AI-for-security evaluation landscape and where it falls short: CyberGym, Cybench, CVE-Bench, BountyBench, CyberSecEval.
Enough software engineering depth to read unfamiliar code, reason about runtime behavior, and hold your own with senior engineers and ML researchers.
Familiarity with how models are post-trained and evaluated, including agentic scaffolds and container-based rollout infrastructure.
Excellent stakeholder management and executive communication skills, with a demonstrated ability to drive alignment across cross-functional organizations.
Sound judgment on dual-use questions, and genuine care about building capability measurement that helps defenders more than attackers.
Entrepreneurial mindset, bias for action, and comfort operating in fast-moving, ambiguous environments.
Preferred
No preferred qualifications listed.