AI Security Engineer: Frontier Model Attacks & Pentesting

Prancer; United States; On-site; USD 150,000 - 230,000; Full time

Job summary

Prancer is hiring AI engineers who understand how models reason and how to exploit systems safely. You will work on the layer where frontier and open-weight models meet a live exploitation engine, teaching models to plan attacks and route them to specialist agents with evidence-backed reporting.

You will design routers, plan agents, and evaluate model integration across web/API, cloud, and Kubernetes environments, ensuring evidence governs outcomes.

Qualifications

• 4+ years in software engineering with hands-on LLM-based systems.
• Real offensive security background: pentesting, red teaming or exploit development.
• Strong Python; Go, Rust, or C is a plus.
• Experience with agent frameworks, tool calling, structured outputs, and prompt/evaluation design.
• Knowledge of web/API exploitation, cloud IAM, Kubernetes, or network protocols.

Responsibilities

• Design and improve the multi-model router to route tasks by efficacy, cost, and policy.
• Build planning/re-planning logic for agents and incorporate new evidence.
• Evaluate frontier/open-weight models for exploit discovery and remediation guidance.
• Create evaluation harnesses on labs and an AWS range to measure success and evidence quality.
• Extend AI-agent attack battery for prompt injection, data exfiltration, and trust boundaries.
• Strengthen safety: scope, kill switch, non-destructive payloads, provenance for actions.

Skills

Python

LLM pipelines

Agent frameworks

Prompt design

Offensive security

Security research

Tools

Mythos

GLM

Qwen

vLLM

SGLang

Job description

Prancer is hiring AI engineers who understand how models reason and how to exploit systems safely. You will work on the layer where frontier and open-weight models meet a live exploitation engine, teaching models to plan attacks and route them to specialist agents with evidence-backed reporting.

You will design routers, plan agents, and evaluate model integration across web/API, cloud, and Kubernetes environments, ensuring evidence governs outcomes.