Strategic Projects Lead, Safety

Handshake

New York (NY)

Hybrid

USD 96,000 - 160,000

Full time

3 days ago

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Benefits offered by this job

Equity

401(k) match

Financial coaching

Parental leave

Fertility benefits

Parental coaching

Medical, dental, and vision insurance

Mental health support

Wellness stipend

Learning stipend

Ongoing development

Remote & Office perks (SF office)

Job summary

Handshake is seeking a Strategic Projects Lead (SPL) on AI Safety & Red Teaming to own large-scale adversarial testing programs that shape frontier AI models before and after release.

You will design, run, and scale red-teaming efforts, supervise Fellows, and deliver insights to strengthen policies and evaluation coverage across multiple labs and harm domains. This role requires strong ownership, clarity, and the ability to operate in ambiguity.

Qualifications

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

Responsibilities

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

Skills

Trust & Safety

Red-teaming

Security research

Policy enforcement

Project management

Analytical thinking

Job description

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.

The Role

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

You Have

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

Bonus Points

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.

We Offer