AI Red Team Engineer

TLDR: We're looking for an AI Red Team Engineer to break LLM-powered systems responsibly, automate the repetitive attacks, and turn their findings into clear evidence that powers customer demos, security reviews, and sales conversations. You'll own hands-on adversarial testing end to end: find the failure, prove it, script it, and write it up. About us White Circle https://whitecircle.ai/ is an AI Safety company building the safety, reliability, and optimization layer for AI systems. At the core of our platform are policies – simple natural-language rules that define what an AI model should and shouldn’t do. We automatically test, enforce, and continuously improve these policies at scale.

• We’ve raised $11M from top funds, founders, and senior leaders at OpenAI, Anthropic, HuggingFace, Mistral, DeepMind, Datadog, Sentry, and others - We process over one hundred million API calls every month - We fine-tune and train our own LLMs so they run faster and cheaper than any open or proprietary model We’re a small, highly focused team.

• Test for jailbreaks, prompt injection, system-prompt and tool leakage, sensitive-data and context leakage, unsafe outputs, policy bypass, tool misuse, excessive agency, resource and token-cost abuse, and business-logic abuse.
• Write lightweight Python to automate attacks, run prompt sets, call model APIs, collect and score responses, and generate repeatable reports.
• Build and maintain an internal attack library: prompts, scenarios, test cases, regression tests, scoring rubrics, and reusable demo cases.
• Turn model failures into clear reports: what happened, why it matters, how to reproduce it, how severe it is, and how to fix it.
• Convert successful attacks into regression tests and product requirements.
• Track new red-team and safety techniques and fold the useful ones into our tests.
• Support GTM by producing strong, credible evidence for customer demos, security reviews, and sales conversations.

• Have a background in QA automation, AppSec, API/security/pen testing, or bug bounty.
• Have strong Python scripting skills.
• Have experience testing APIs, web apps, backends, or SaaS products.
• Are hands-on with LLMs, prompts, system instructions, RAG, agents, and tool/function calling.
• Understand LLM-specific abuse vectors (prompt injection, jailbreaks, data leakage, tool misuse, excessive agency, token-cost exhaustion).
• Can find bypasses, abuse edge cases, chain failures, and reason about real-world impact.
• Can separate real customer risk from low-impact prompt tricks.
• Write clear, reproducible bug reports in clear English.
• Can move fast without perfect requirements.
• Hold a firm ethical line: you red-team to make systems safer, operate within scope and the law, and don't produce or traffic in genuinely harmful material.

• Experience with modern LLM red-teaming automated agents and pipelines.
• Familiarity with LangChain, LangGraph, LlamaIndex, RAG pipelines, AI agents, tool/function calling, and LLM-as-judge evaluation.
• Familiarity with OWASP LLM Top 10, OWASP Web Top 10, MITRE ATLAS, or other AI security taxonomies.
• Experience testing RAG systems, AI agents, tool-calling workflows, browser agents, or internal copilots.
• Experience writing customer-facing security reports.
• Experience with trust & safety, abuse prevention, fraud, moderation, or platform security.
• Experience building eval pipelines, regression suites, dashboards, or CI-friendly security tests.
• A track record in CTFs, red-team competitions, or responsible-disclosure / bounty programs.

• Showcase a portfolio of Python scripts that automate security testing against APIs or LLMs; concrete examples speak louder than a resume.
• Highlight any bug bounty or CTF achievements, especially those involving prompt injection or model exploitation.
• Be prepared to discuss specific LLM abuse vectors you’ve mitigated and the reasoning behind severity assessments.
• During interviews, demonstrate your ability to write clear, reproducible bug reports on the spot.
• Emphasize ethical red‑team practices—explain how you stay within scope and legal boundaries.
• If asked about GTM support, illustrate how technical evidence can be translated into persuasive sales material.
• Watch for vague promises about “flexible hours” without clear remote‑work policies; ask about communication cadence and team overlap expectations.