Member of Technical Staff – Research Engineering

We find and fix every vulnerability in our customers' applications. When a new AI model is released, we run it against their applications before attackers do, and close what it finds.

Wakefield is an applied research lab working on AI-native security services. We build security agents and publish how they work. Our methods, benchmark and raw data are public so anyone can check them.

• Source code optional: blackbox agents sign in as every role and test the running app. Whitebox agents read the code on every pull request.

• If Wakefield can't prove it, the customer never sees it: agents replay each finding in a real browser and record it. Then Wakefield writes the fix, ready to open as a pull request, and retests it. We have SLAs for zero false positives.

• One service that replaces DAST, SAST, SCA, SBOM, and the tests companies run in-house or buy in.

Wakefield is only as good as what its agents find, and how sure we are that it's real. You'll own how the agents get better: the benchmark and evals, the harness, the agent that proves each finding, and the loop that turns every miss and every false alarm into a verified improvement. When a new frontier model comes out, you'll be the one who finds out what it can do against real applications.

• Benchmark applications with known vulnerabilities, and evals that measure every change to the agents.

• Agents that learn who should see what in an application and try to cross user, role and tenant lines.

• The validation agent that replays each finding in a real browser and decides whether it is real.

• Turning production traces into cases we can rewind, change one thing, and run forward.

• Running every new model through the harness and the evals within days of its release.

• Credit assignment over long scans: which steps found the bug, and which wasted time.

• 2+ years of engineering experience, or equivalent depth from research, open source or your own projects

• Time at a fast-growing startup

• You've shipped applied ML systems from scratch as an engineer, not only papers

• You've run evals and experiments rigorously against production systems

• Strong fundamentals: ML evaluation, experimental design, statistics, LLM agents

• You use coding agents every day and still understand every line you ship

• You build and tinker for fun because you genuinely care about how the technology works

• Harness, post-training or applied AI experience at a lab or applied AI team

• Published research on agents or security, or a CTF background

• Location:
  
  San Francisco, in person. Most of the team is in the office every day.

• Compensation:
  Competitive salary and meaningful equity.

• Visa:
  We sponsor visas and support you through the immigration process.