Red Teaming Expert – AI Safety, Execution, QA Tooling Support
mpathic
full-time
Posted on:
Location Type: Remote
Location: Washington • United States
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Tech Stack
About the role
- Execute advanced adversarial testing and produce high-quality, reproducible findings.
- Partner with Product/Engineering to improve the QA tooling and delivery pipeline.
- Design and run adversarial tests across multiple failure modes.
- Create realistic multi-turn scenarios and produce clear, reproducible findings.
- Participate in QA review cycles to improve consistency and signal.
- Help refine rubrics and evaluation standards over time.
- Collaborate with TPM + Engineering to build scalable quality systems.
- Identify bottlenecks and propose automation.
Requirements
- 3+ years in AI red teaming / model evaluation / trust & safety / abuse testing / security testing / adversarial testing / content integrity / policy enforcement QA
- Strong ability to generate adversarial prompts and identify realistic failure modes.
- Excellent writing skills: crisp, structured, and audit-friendly documentation.
- Sound judgment around safety severity.
- Comfortable working cross-functionally with Product/Engineering.
- Experience red teaming LLMs, tool-using models, or agentic systems is preferred.
- Familiarity with: prompt injection, indirect injection attacks, data exfiltration, privacy testing, evaluation datasets, gold sets, benchmark design.
- Experience supporting QA systems at scale (review flows, IRR, sampling plans).
- Ability to contribute technically to pipeline work: writing scripts, working with APIs, SQL, dashboards, or data tooling.
Benefits
- Health insurance
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
adversarial testingmodel evaluationsecurity testingautomationwriting scriptsAPIsSQLdata toolingbenchmark designprivacy testing
Soft Skills
excellent writing skillssound judgmentcross-functional collaborationability to generate promptsidentifying failure modes