Senior AI Product Manager, Cybersecurity

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.

Security is where the hardest problems in agentic AI now sit. An agent that can find a vulnerability, prove it reproduces, and patch it without breaking the system is doing work that takes a skilled human days. Measuring that honestly requires reproducible execution environments at scale and practitioners who have actually done the work.

Scale has the first, proven across SWE-Bench Pro, SWE Atlas, and our contributions to the Terminal-Bench lineage. You will build the second.

- 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.

- 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. CyberGym sets the bar we hold ourselves to - real vulnerabilities sourced at scale, execution-grounded verification, and tasks hard enough that frontier agents still clear only about a fifth of them.

- 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.