AI Red Teamer: Offensive Security for Safe AI

Handshake; Seattle (WA); On-site; USD 120,000 - 210,000; Full time

Job summary

Handshake is seeking a Cybersecurity Red Teamer to evaluate whether AI models can be manipulated into generating malware, exploits, or attacker guidance. You will craft adversarial prompts and multi-turn interactions to simulate threat actor use of LLMs, then assess output risk and exploitability with deep cybersecurity expertise.

Day-to-day work includes designing prompts, evaluating model outputs for functional robustness, testing across multiple offensive categories, and collaborating with

Qualifications

• Experience in offensive security, penetration testing, red teaming, or incident response.
• Ability to read, write, and evaluate code across common languages used in offensive tooling.
• Understanding of MITRE ATT&CK, OWASP, and related frameworks.
• Ability to assess functional correctness and real-world exploitability of model outputs.
• Strong written and verbal communication for technical risk framing.

Responsibilities

• Design adversarial prompts to test AI models across cyber kill chains (reconnaissance to exfiltration).
• Evaluate model-generated code and outputs for functionality and exploitability.
• Test across malware, vulnerability exploitation, social engineering, credential harvesting, and exfiltration techniques.
• Probe dual-use boundaries and assess safe-guard effectiveness.
• Simulate attacker personas across skill levels to gauge risk escalation.
• Document findings with clear reasoning on what helps or harms attackers.

Skills

Offensive security

Penetration testing

Red teaming

Threat modeling

Adversarial thinking

Written communication

Education

Security certifications (OSCP/OSCE/GPEN)

Tools

Python

PowerShell

Bash

C/C++

LLM tooling

Job description

Handshake is seeking a Cybersecurity Red Teamer to evaluate whether AI models can be manipulated into generating malware, exploits, or attacker guidance. You will craft adversarial prompts and multi-turn interactions to simulate threat actor use of LLMs, then assess output risk and exploitability with deep cybersecurity expertise.

Day-to-day work includes designing prompts, evaluating model outputs for functional robustness, testing across multiple offensive categories, and collaborating with