AfterQuery is an applied research lab curating data solutions for foundation model development. We serve every frontier AI lab with the mission of delivering the best data to power the best models. In doing so, we can make expertise that once took a lifetime to build available to anyone who needs it.
Our customers are the ones building the foundation models themselves and our work sits directly in the loop of how those systems improve. This is a rare opportunity to join a company at a defining moment in AI. Forbes reported that we could be YC's fastest unicorn, reportedly raising at a $3.2 billion valuation. We're based in San Francisco and backed by leading investors including Altos Ventures, BoxGroup, and Y Combinator and angels from Google DeepMind, OpenAI, Anthropic, Meta Superintelligence Labs, and Microsoft AI.
Why ApplyMassive Opportunity: Forbes reported that we could be YC's fastest unicorn, reportedly raising at a $3.2 billion valuation, and we're not slowing down.
Founding Impact: You will own and architect core infrastructure systems that power our platform from the ground up.
Equity & Growth: Competitive salary and meaningful equity. As we scale, you’ll have the opportunity to shape the engineering organization and lead major technical initiatives.
Strong Team: Our founding team has experience from Citadel Securities, Meta, Google, Silver Lake, and Morgan Stanley — work alongside world-class engineers and researchers.
ResponsibilitiesProject 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.
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.
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.
Health Insurance: Medical, Vision, Dental
401(k) with Employer Match
Daily Meals: Daily UberEats Stipend
Monthly Wellness Stipend
We are an equal opportunity employer committed to providing a workplace free from discrimination and harassment. Employment decisions are made without regard to legally protected characteristics under applicable federal, state, or local law.
We comply with applicable pay transparency requirements and provide compensation ranges based on the position, qualifications, experience, and other relevant factors. Reasonable accommodations are available to qualified individuals with disabilities and for sincerely held religious beliefs, as required by law. This job description is intended to describe the general nature and level of work performed and is not an exhaustive list of all duties, responsibilities, qualifications, or working conditions associated with the position. We reserve the right to modify this job description as business needs change.
Skills Required
- 2+ years of experience in AI safety, red teaming, trust and safety, adversarial machine learning, or security research at a frontier AI lab, FAANG company, top security firm, or equivalent.
- Hands-on adversarial experience including model jailbreaking, frontier model stress testing, prompt injection research, offensive security, penetration testing, bug bounty, CTFs, or trust and safety investigations.
- Fluency with harm taxonomies, evaluation design, safety frameworks, and model policy.
- High agency and ability to execute in ambiguous environments.
- Strong technical and professional writing skills.
- Genuine passion for AI and interest in entrepreneurship.
- Demonstrated track record of competitive success.
- Strong leadership and communication skills.
- Python proficiency and ability to write production-quality code.
- Published safety, alignment, or security research.
- Track record in bug bounty, CTF, or model red-teaming competitions.
- Experience building automated red-teaming or evaluation pipelines.
- Expertise in a high-consequence risk domain such as cyber, bio, fraud, child safety, or influence operations.
- Experience using AI tools to automate workflows and build products.
AfterQuery Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about AfterQuery and has not been reviewed or approved by AfterQuery.
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Fair & Transparent Compensation — Pay is considered attractive when work is accepted, with public role postings and materials indicating strong compensation across expert projects and core employee roles. The experts track also highlights transparent pay rates and approval-linked payouts that do land.
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Healthcare Strength — Employee materials indicate medical, vision, and dental insurance are provided. Job postings reference a comprehensive package consistent with standard coverage.
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Wellbeing & Lifestyle Benefits — Daily meal stipends, commute support via Uber credits, and a monthly wellness stipend (including gym membership coverage) are prominently advertised. These lifestyle perks suggest attention to day-to-day convenience and wellbeing.
AfterQuery Insights
What We Do
AfterQuery is an applied research lab curating data solutions to accelerate foundation model development.



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