Lead Security Researcher

Pi.Security · San Francisco, CA · Full-time · Onsite, 5 days a week

About Pi.Security

Pi is building an agentic product security platform for teams that need to secure software at the speed they build it.

Modern development is accelerating, but security knowledge is still scattered across code, tickets, documents, incidents, reviews, and the people who remember why decisions were made. Pi turns that context into institutional security memory, helping teams triage faster, remediate in context, prevent repeat vulnerability classes, and embed security guardrails where engineering work already happens. We are building for a future where security is not a blocker at the end of the development process. It is part of how software gets designed, reviewed, shipped, and improved.

Pi was recognized on Fast Company's 2026 Next Big Things in Tech list and covered by Forbes!

About the Role

You will lead security research at Pi. This is not a bug hunting role. Your job is to invent the approaches that become product capabilities: new ways to detect, triage, and remediate vulnerabilities using LLMs and program analysis, proven on real data before a customer ever sees them.

You own the path from idea to shipped capability. You form the hypothesis, build the proof of concept, measure whether it actually works, and partner with engineering until it is in the product. You also own the quality bar: the benchmarks and evaluations that decide whether what we ship is good enough to put in front of customers.

You will set the research agenda, not just execute one. As the team grows, you will shape how research works here.

This role is based in our San Francisco office, five days a week. We build in person, alongside the founding team, and that is a deliberate choice.

What You'll Do

• The research agenda - Identify where new approaches can meaningfully beat the state of the art in vulnerability detection, triage, and remediation, then decide what we pursue and what we kill. No one hands you a backlog.

• Approaches that ship - Build proofs of concept for new detection and remediation techniques, validate them against real world code and data, and carry the winners through to production with engineering. You are accountable for ideas becoming product, not staying research.

• The quality bar - Design the datasets, benchmarks, and evaluation pipelines that measure precision, coverage, and false positive rates. Nothing reaches customers past a bar you have not signed off on, and if quality slips, you catch it first.