Strategic Projects Lead, Safety

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.

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.

As a Strategic Projects Lead (SPL) on AI Safety & Red Teaming, you will own the execution of large-scale adversarial testing programs that directly develop frontier AI models before and after release. You will design and run structured efforts to push models to failure, spanning work such as evaluating harm and policy categories against frontier labs’ own policy specifications, developing jailbreak and adversarial technique pipelines, and extending red-teaming into agentic settings, among other methods, then work directly with frontier labs to turn what you find into sharper policies and stronger evaluation coverage.

Working hands-on with leading AI labs, you will make real-time decisions that affect delivery quality, program scope, and long-term customer relationships. This is a high-ownership, outcomes-driven role for operators who thrive in ambiguity, move fast with incomplete information, and are accountable for results at scale.

• Own end-to-end execution of red-teaming and safety evaluation programs spanning sensitive, high-severity harm categories, from scoping through delivery

• Run multiple programs across different frontier labs and policy domains, each with its own scope, harm categories, and stakeholders

• Design and run adversarial testing methodologies, such as jailbreak technique development and iterative push-to-failure testing, to surface model vulnerabilities against frontier labs’ policies

• Extend red-teaming methods into agentic contexts, evaluating how models behave under adversarial pressure when operating with tools and multi-step autonomy

• Lead and coordinate teams of expert Fellows executing sensitive, high-stakes testing work, maintaining precision and consistency across harm categories, languages, and markets

• Partner directly with policy leads at frontier AI labs to identify coverage gaps, ambiguous classifications, and emerging risk areas, and help shape policy documents based on findings

• Synthesize testing data into insight reports that surface trends, systemic gaps, and recommended areas of policy and evaluation development

• Design and adapt staffing models for your red-teaming workforce (team size, skill mix, training, and incentive structures) to improve throughput and delivery reliability as program requirements evolve

• 2+ years of experience in trust & safety, red-teaming, security research, policy enforcement, or a related technical/analytical field

• Excellent project and workforce management skills, comfortable leading and directing teams of red-teamers and testers against tight timelines and shifting scope

• Strong analytical and first-principles problem-solving skills, comfortable operating in ambiguous, fast-changing testing environments

• Working familiarity with adversarial testing concepts (jailbreak techniques, prompt-based exploits) or a fast ability to pick them up

• Exceptional communication and stakeholder management skills, including with senior customers and policy teams at frontier AI labs

• High ownership mindset with pride in end-to-end accountability

• Curiosity and ability to quickly learn technical AI concepts, model behavior, and industry trends

• Experience red-teaming, jailbreaking, or adversarially evaluating LLMs or agentic AI systems

• Subject matter expertise in high-severity policy harms (e.g., CBRN, cyber, violent extremism)

• Experience running testing or evaluation programs

• Background in security research, offensive security, or AI safety research

Note: This role involves exposure to sensitive or explicit content (e.g., violent, sexual, or otherwise disturbing material) as part of safety and policy evaluation work.