Senior Security Researcher

About the Role

UNLEASH AI INNOVATORS, SECURELY Software is being rewritten in real time. AI agents are starting to write more code than humans do, at a speed no security team has ever had to match, and that single shift is opening up the biggest opportunity our industry has seen in a decade. We're not bracing for it, we're building the company that owns it. Snyk is building the next generation security platform where security isn't a set of tools people operate. It's a team of security agents working alongside your engineers, finding, fixing, and governing risk at the speed software is now built. These agents work alongside both the company build and the agents they adopt. Security stops being a queue of work waiting for people, and becomes a coordinated workforce companies direct and trust. Research and exploit weaknesses in LLM applications and autonomous agents. Build attack tooling, agent harnesses, and reproducible evaluations that turn offensive research into measurable improvements in AI security and improve on Snyk’s offensive security product offering. Looking for candidates with experience in the following areas: - AI red teaming: Prompt injection, jailbreaks, data leakage, tool abuse, and multi-step adversarial attacks. - Offensive security: Web/API penetration testing, authentication and authorization flaws, business logic attacks, and exploit chaining. - LLM fundamentals: Model behavior and limitations, inference, context management, retrieval, and tool calling. - Harness engineering: Agent orchestration, tool integrations, state management, sandboxed execution, and reproducible runs. - Agent security: Trust boundaries, permissions, memory poisoning, scope enforcement, and guardrail bypass testing. - Evals and benchmarks: Realistic attack scenarios, ground truth, reliable graders, dataset quality, and regression suites. - Experimental design: Controlled comparisons, repeated trials, attack success rates, precision/recall, and cost/performance measure

Key Responsibilities

• AI red teaming: Prompt injection, jailbreaks, data leakage, tool abuse, and multi-step adversarial attacks.
• Offensive security: Web/API penetration testing, authentication and authorization flaws, business logic attacks, and exploit chaining.
• LLM fundamentals: Model behavior and limitations, inference, context management, retrieval, and tool calling.
• Harness engineering: Agent orchestration, tool integrations, state management, sandboxed execution, and reproducible runs.
• Agent security: Trust boundaries, permissions, memory poisoning, scope enforcement, and guardrail bypass testing.
• Evals and benchmarks: Realistic attack scenarios, ground truth, reliable graders, dataset quality, and regression suites.

Requirements & Qualifications

• Strong problem solving foundations, knowledge of modern engineering practices, and interest in technology systems.

Perks & Benefits

• Official mentor support
• verified hiring timeline
• and career progression.