AI Red Teamer, Cybersecurity

ABOUT HANDSHAKE Handshake was founded on a simple belief that everyone deserves a path to a great career, regardless of where they went to school or who they know. Today, we power 25 million job seekers, 1 million+ employers, and 1,600 educational institutions. In 2025, we started Handshake AI and built the fastest-growing AI data business in history. We work directly with frontier AI lab researchers to create evaluations, publish benchmarks, and push the boundary of data. We’ve grown from $0 to ~$1B run rate and pay ~$60M to over 30K individuals every month. Why join Handshake now: - Shape how every career evolves in the AI economy, at global scale, with impact your friends, family and peers can see and feel - Partner hand-in-hand with world-class AI labs, Fortune 500 partners and the world’s top educational institutions - Work together with engineers, scientists, operators, and more from Palantir, Meta, Scale AI, and former YC founders - Build a massive, fast-growing business with billions in revenue About Handshake AI Human data is the core infrastructure to AI advancement. Frontier AI labs currently improve model capabilities with various data-intensive post-training techniques. We believe that data spend for AI training will increase by 3-5x in the next few years and continue for much longer as models take on new domains. Handshake AI supports all of the frontier AI labs, working on their most complex data at the largest scale. ABOUT THE ROLE As a Cybersecurity Red Teamer, you will evaluate whether AI models can be manipulated into generating functional malware, viable exploit code, attack tooling, or step-by-step operational guidance that would give a threat actor meaningful uplift in carrying out cyberattacks. Your job is to find the gaps between what a model's safety guardrails are supposed to block and what a skilled adversary can actually extract. This role requires you to think like an attacker who happens to have access to a very capable AI assistant. You will craft adversarial prompts and multi-turn interaction chains that simulate how real threat actors, from script kiddies to APT operators, might use LLMs to accelerate reconnaissance, weaponization, exploitation, lateral movement, persistence, and exfiltration. You will then evaluate whether the model's output is genuinely dangerous or just surface-level noise. Deep cybersecurity expertise is essential. The value you bring is the ability to look at a model-generated payload, exploit chain, or attack plan and assess whether it would actually work, how much refinement it would need, and what class of attacker it would meaningfully help. DAY-TO-DAY RESPONSIBILITIES - Design technically grounded adversarial prompts that test whether models provide meaningful uplift across the cyber kill chain (reconnaissance through exfiltration and impact) - Evaluate model-generated code and technical output for functional correctness, assessing whether outputs represent real exploits, plausible attack tooling, or non-functional noise - Test model behavior across offensive categories including malware generation, vulnerability exploitation, social engineering content, credential harvesting, privilege escalation, C2 infrastructure setup, and data exfiltration techniques - Probe dual-use boundaries, testing how models handle queries that blend legitimate security research, penetration testing, and defensive operations with offensive applications - Simulate attacker personas at varying skill levels (opportunistic, intermediate, advanced/APT) to assess how model risk scales with user sophistication - Test multi-step and multi-turn attack chains, including scenarios where early turns establish benign context before pivoting to malicious requests - Score model responses against structured harm taxonomies and severity rubrics calibrated to real-world exploitability - Document findings with clear technical reasoning, including what a response gets right, what it gets wrong, and what level