AI Red Teamer, CBRNE (Remote)

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 CBRNE Red Teamer, you will evaluate whether AI models appropriately handle queries related to chemical, biological, radiological, nuclear, and explosive threats. Your job is to probe models for dangerous knowledge gaps in their safety guardrails, testing whether they can be manipulated into providing meaningful uplift toward the creation, acquisition, or deployment of weapons or hazardous materials.

This work sits at the intersection of AI safety and national security. You will use your domain expertise to craft realistic, technically grounded adversarial scenarios that stress-test model defenses far beyond what a non-expert could attempt. The goal is not to generate harmful content, but to find and document the places where models fail to refuse, hedge, or redirect appropriately so that labs can fix them before those failures reach the real world.

This role requires deep subject matter expertise in at least one CBRNE domain, strong ethical judgment, and the ability to think like a sophisticated threat actor while operating within a structured evaluation framework.

DAY-TO-DAY RESPONSIBILITIES

• Design technically grounded adversarial prompts that test whether models provide meaningful uplift toward CBRNE threats

• Evaluate model outputs for technical accuracy, assessing whether responses contain genuinely dangerous information versus superficial or publicly available knowledge

• Probe dual-use knowledge boundaries, testing how models handle queries that blend legitimate scientific, medical, or industrial use cases with potential weapons applications

• Test multi-step and multi-turn attack chains that simulate how a motivated actor might extract dangerous information incrementally

• Score model responses against structured harm taxonomies and severity rubrics calibrated to real-world risk

• Document findings with clear technical reasoning, including what a response gets right, what it gets wrong, and why the failure matters

• Identify and articulate the difference between information that is freely available in open literature and information that constitutes genuine uplift beyond baseline

• Contribute to the development and refinement of CBRNE-specific evaluation frameworks and threat models

• Collaborate with other red teamers, AI researchers, and policy teams to translate findings into actionable model improvements

• Stay current on evolving model capabilities, jailbreak techniques, and relevant developments in your domain

DESIRED CAPABI

Responsibilities

• Design technically grounded adversarial prompts that test whether models provide meaningful uplift toward CBRNE threats
• Evaluate model outputs for technical accuracy, assessing whether responses contain genuinely dangerous information versus superficial or publicly available knowledge
• Probe dual-use knowledge boundaries, testing how models handle queries that blend legitimate scientific, medical, or industrial use cases with potential weapons applications
• Test multi-step and multi-turn attack chains that simulate how a motivated actor might extract dangerous information incrementally
• Score model responses against structured harm taxonomies and severity rubrics calibrated to real-world risk
• Document findings with clear technical reasoning, including what a response gets right, what it gets wrong, and why the failure matters
• Identify and articulate the difference between information that is freely available in open literature and information that constitutes genuine uplift beyond baseline
• Contribute to the development and refinement of CBRNE-specific evaluation frameworks and threat models
• Collaborate with other red teamers, AI researchers, and policy teams to translate findings into actionable model improvements
• Stay current on evolving model capabilities, jailbreak techniques, and relevant developments in your domain

Requirements

• Deep subject matter expertise in at least one CBRNE domain
• Strong ethical judgment
• Ability to think like a sophisticated threat actor while operating within a structured evaluation framework