About this opportunity
Reinforced Learning lists this Strategic Projects Lead - AI Safety opportunity in town of tonawanda, New York. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Project and client management: Partner directly with the safety, alignment, and trust & safety teams at frontier AI labs — scoping their hardest data problems, translating them into deliverable programs, and driving revenue.
Manage expert teams: Recruit and lead specialized contributors — red teamers, security researchers, trust & safety practitioners, and domain risk experts — and hold a high bar on the quality of what they produce.
Shape how frontier models are made safe: Design the specifications behind adversarial datasets, jailbreak and attack taxonomies, refusal-boundary and over-refusal sets, and safety benchmarks that labs use to measure and improve model behavior.
Build the red teaming machinery: Stand up repeatable methodologies — human red team campaigns, automated attack generation, coverage tracking against harm taxonomies — rather than one-off deliverables.
End-to-end execution: Own projects from first conversation through final delivery, in a fast-moving environment where the spec often does not exist yet.
Operational impact: Support initiatives across building, analysis, coordination, and execution as we scale the safety practice.
Responsibilities
Project and client management: Partner directly with the safety, alignment, and trust & safety teams at frontier AI labs — scoping their hardest data problems, translating them into deliverable programs, and driving revenue.
Manage expert teams: Recruit and lead specialized contributors — red teamers, security researchers, trust & safety practitioners, and domain risk experts — and hold a high bar on the quality of what they produce.
Shape how frontier models are made safe: Design the specifications behind adversarial datasets, jailbreak and attack taxonomies, refusal-boundary and over-refusal sets, and safety benchmarks that labs use to measure and improve model behavior.
Build the red teaming machinery: Stand up repeatable methodologies — human red team campaigns, automated attack generation, coverage tracking against harm taxonomies — rather than one-off deliverables.
End-to-end execution: Own projects from first conversation through final delivery, in a fast-moving environment where the spec often does not exist yet.
Operational impact: Support initiatives across building, analysis, coordination, and execution as we scale the safety practice.
Required Qualifications
2+ years in AI safety, red teaming, trust & safety, adversarial ML, or security research; at a frontier AI lab, FAANG, top security firm, or equivalent.
Hands-on adversarial experience: jailbreaking or stress-testing frontier models, prompt injection research, offensive security, penetration testing, bug bounty, CTFs, or trust & safety investigations and enforcement.
Fluency with how safety work is actually structured (harm taxonomies, evaluation design, safety frameworks and model policy).
High agency and the ability to execute in ambiguity; comfort defining the problem before solving it.
Strong writing. Much of this job is specifying risk precisely enough that fifty other people can execute against it.
Genuine passion for AI and interest in entrepreneurship.
Demonstrated track record of competitive success.
Strong leadership and communication skills.
Python proficiency and the ability to write production-quality code.
Preferred Qualifications
Published safety, alignment, or security research.
Track record in bug bounty, CTF, or model red teaming competitions.
Experience building automated red teaming or eval pipelines.
Depth in a high-consequence risk domain (cyber, bio, fraud and financial crime, child safety, influence operations).
Experience using AI tools to automate workflows and build products.
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Who can apply
Review the original listing for work authorization, qualifications and employer requirements.