Member of Technical Staff – Research Engineering

Alkonos, Inc.; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Competitive salary

Meaningful equity

Visa sponsorship

Job summary

Wakefield is a San Francisco–based applied research lab building AI-native security services. You will own how the agents get better—the benchmark, the eval harness, the validator that proves each finding, and the loop that turns misses into verified improvements.

When a new frontier model arrives, you’ll determine what it can do against real applications, running rigorous tests and turning results into robust improvements. In-person role with visa sponsorship and equity.

Qualifications

• 2+ years of engineering experience or equivalent depth from research.
• Experience at a fast-growing startup is preferred.
• Shipped applied ML systems from scratch as an engineer.
• Ran evals and experiments rigorously against production systems.
• Strong fundamentals: ML evaluation, experimental design, statistics, LLM agents.
• You use coding agents daily and understand every line you ship.
• You build and tinker for fun because you care about how the technology works.

Responsibilities

• Benchmark apps with known vulnerabilities and evals that measure changes to the agents.
• Develop agents that learn across users and tenant boundaries.
• Maintain the validation agent that replays findings in a real browser.
• Turn production traces into testable cases and rerun experiments.
• Run every new model through the harness and evals within days of release.
• Credit assignment over long scans to identify effective bug-finding steps.

Skills

ML evaluation

Experimental design

Statistics

LLM agents

Coding agents

Tools

Harness

Job description

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.

About Wakefield

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.

The role

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.

What you'll work on

• 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.

Requirements

• 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

Nice to have

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

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

Logistics

• 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.