Strategic Projects Lead - AI Safety

Reinforced Learning; Town of Tonawanda (NY); On-site; USD 180,000 - 260,000; Full time

Job summary

Reinforced Learning seeks a leader to partner with frontier AI labs on safety, alignment, and risk management. You will recruit expert contributors, design adversarial datasets, and build repeatable red-teaming systems that drive measurable safety outcomes.

Responsibilities include owning end-to-end projects, shaping safety specifications, and scaling efforts across teams while delivering revenue and high-quality results.

Qualifications

• 2+ years in AI safety, red teaming, trust & safety, adversarial ML, or security research at frontier AI labs, 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.
• Fluency with how safety work is 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. Ability to specify risk precisely for others to execute.
• Genuine passion for AI and entrepreneurship.
• Demonstrated track record of competitive success.
• Strong leadership and communication skills.
• Python proficiency and ability to write production-quality code.

Responsibilities

• Project and client management: Partner directly with 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 maintain a high bar on quality.
• Shape how frontier models are made safe: Design specifications behind adversarial datasets, jailbreak and attack taxonomies, refusal-boundary and over-refusal sets, and safety benchmarks labs use to measure model behavior.
• Build the red teaming machinery: Establish repeatable methodologies — human red team campaigns, automated attack generation, coverage tracking against harm taxonomies.
• 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.

Skills

Python proficiency

Production-quality code

Leadership

Communication skills

Strong writing

Ambiguity tolerance

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.