---
title: 'Senior AI Product Manager, Cybersecurity at Scale AI'
canonical: 'https://feeny.ai/job/senior-ai-product-manager-cybersecurity-scale-ai-new-york-738726w913vn'
type: 'job'
last_seen: '2026-09-09'
---

# Senior AI Product Manager, Cybersecurity at Scale AI

- **Company:** Scale AI
- **Location:** New York, NY / San Francisco, CA
- **Compensation:** $206k–$257k
- **Posted:** 2026-08-04
- **Last confirmed live:** 2026-09-09
- **Apply:** https://job-boards.greenhouse.io/scaleai/jobs/4720213005

## Job description

Scale has been the leading AI data foundry, helping fuel the most exciting advancements in AI, including frontier model training, enterprise adoption, defense applications, and autonomous vehicles. Our mission is to develop reliable AI systems for the world's most important decisions

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.

You Will

- 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.

Ideally, You'd Have

- 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.

Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend.

Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is: $205,600—$257,000 USD

PLEASE NOTE: Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants.

About Us:

At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst & Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications.

We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.

We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's [Know Your Rights poster](https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf) for additional information.

We comply with the United States Department of Labor's Pay Transparency provision.

PLEASE NOTE: We collect, retain and use personal data for our professional business purposes, including notifying you of job opportunities that may be of interest and sharing with our affiliates. We limit the personal data we collect to that which we believe is appropriate and necessary to manage applicants’ needs, provide our services, and comply with applicable laws. Any information we collect in connection with your application will be treated in accordance with our internal policies and programs designed to protect personal data. Please see our [privacy policy](https://scale.com/legal/privacy) for additional information.

## About Scale AI

## Company Overview
- **One-liner**: Scale AI provides high-quality data, RLHF, model evaluations, and full-stack AI infrastructure to help enterprises and governments build, deploy, and oversee reliable AI systems.
- **Entity Type**: Private (49% non‑voting stake owned by Meta Platforms as of June 2025; total funding $1.6B across eight rounds)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2016
- **Founders**: Alexandr Wang, Lucy Guo (Wang left in June 2025; current CEO is Jason Droege)

## Core Business
- **Primary industry**: AI infrastructure, data annotation, large language model (LLM) evaluation, enterprise AI deployment
- **Target customers**: B2B – Enterprise and government organizations; also serves leading AI labs (e.g., OpenAI, Google DeepMind, Meta, Microsoft, General Motors)
- **Mission**: “Develop reliable AI systems for the world's most important decisions.”

## Products & Services
- **Data at Scale**: High-quality training data, annotations, and RLHF for advanced AI models (SaaS + human-in-the-loop service)
- **Evaluations**: Rigorous model evaluations, benchmarking, and red‑teaming to measure and improve AI performance (service)
- **Applied AI**: Full‑stack AI systems that help enterprises and governments build, deploy, and oversee reliable AI (SaaS + consulting)
- **Safety, Evaluation and Alignment Lab**: Research arm focused on LLM alignment and safety (internal R&D; also co‑created the “Humanity's Last Exam” benchmark)
- **Subsidiaries**: Remotasks (computer vision and autonomous vehicle data labeling), Outlier (LLM data annotation)

## Market Standing
- **Valuation**: $29B (as of 2025 – cited on scale.com)
- **Key Metric**: Total Funding – $1.6B (including the $14B Meta investment that acquired a 49% non‑voting stake in June 2025)
- **Notable Investors/Partners**: Meta Platforms (49% owner), with commercial customers including Google, Microsoft, Meta, General Motors, OpenAI, and Time. Also works with U.S. and Qatari governments.
- **Growth Signals**:
  - Headcount: Scale.com cites “1,000+” employees; LinkedIn reports 3,751 employees (+32.1% YoY).
  - 90% of the world’s leading generative AI model builders are powered by Scale.
  - 15 billion human decisions used to train AI models; $1 billion paid to contributors globally.
  - Active job postings: 284+ (as of July 2025), with strong hiring in enterprise engineering, AI agents, and solutions roles.

## Competitive Advantages
- **Data moat**: Scale’s proprietary Data Engine and access to millions of human‑annotated decisions create high‑quality training data that competitors cannot easily replicate.
- **Trust & adoption**: Used by 90% of leading GenAI builders; runs private benchmarks for the most ambitious AI companies.
- **Government credibility**: Direct contracts with the U.S. Department of Defense and international governments (e.g., Qatar) – a high‑barrier entry point.
- **Full‑stack offering**: From raw data annotation to LLM evaluation and end‑to‑end applied AI deployment, Scale covers the entire AI lifecycle.
- **Research leadership**: In‑house Safety, Evaluation and Alignment Lab; co‑creator of the “Humanity's Last Exam” benchmark.

## Strategic Focus
- **Enterprise GenAI agents**: Ramping up “AgentOps” and “Frontier Agents” engineering teams (many open roles in SF, NY, London, Budapest).
- **Healthcare & life sciences**: Hiring dedicated GTM leaders and AI strategists for healthcare vertical.
- **International expansion**: Growing offices in London, Budapest, Mexico City, and Washington DC.
- **Safety and alignment**: Continued investment in red‑teaming, model evaluations, and government‑focused AI safety contracts.

## Why Work Here
- **Mission‑driven**: “Develop reliable AI systems for the world’s most important decisions” – directly shaping frontier AI capabilities.
- **Compensation & benefits**: Comprehensive health, dental, vision, mental health services; generous PTO; annual learning & development stipend; parental leave; ERGs; guest‑friendly offices with happy hours, game nights, book clubs.
- **Engineering culture**: Credos such as “Write the Market,” “Find the 20%,” “Earn Customer Love,” and “Quality is Our Cheat Code” emphasize impact over effort, customer obsession, and structured thinking.
- **Growth trajectory**: Rapid headcount growth (+32%), major Meta investment, and expansion into new verticals signal a company in high‑growth mode.
- **Flexible work**: Offices in SF, NY, London, Budapest, Mexico City, DC; the careers page highlights “flexible environment,” though specific remote/hybrid policy is not explicitly stated.

## Sources
1. [scale.com](https://scale.com/about)
2. [scale.com](https://scale.com/)
3. [scale.com/careers](https://scale.com/careers)
4. [linkedin.com](https://www.linkedin.com/company/scaleai)
5. [greenhouse.io](https://job-boards.greenhouse.io/scaleai)

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