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. Since raising our $30M Series A at a $300M valuation, AfterQuery has grown well over a $100M revenue run rate.
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:
We were one of the fastest-growing YC companies in our batch, and we believe we can become one of the fastest-growing YC companies of all time.
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.
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.
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.