AI Engineer, Offensive Security

Prancer builds continuous frontier attack validation through an autonomous, multi-agent penetration testing platform. The AI Engineer, Offensive Security will develop model routing, agent planning, model evaluation, and safety mechanisms for AI-driven exploitation, while supporting private and sovereign deployments.

Responsibilities:

• Design and improve the multi-model router that decides which model, or which deterministic path, handles each attack task based on efficacy, cost, and deployment policy
• Build agent planning and re-planning logic so SwarmHack adapts as new evidence appears. That includes captured credentials seeding authenticated re-crawls and discovered networks feeding lateral pivots
• Integrate and evaluate frontier and open-weight models (Mythos, GLM, Qwen, and others) for exploit discovery, attack chaining, and remediation guidance
• Build evaluation harnesses against vulnerable labs and our 200-host AWS range to measure exploit success, false positives, and evidence quality over time
• Extend the AI-agent attack battery that tests LLM applications, RAG pipelines, tool use, and MCP/A2A integrations for prompt injection, tool abuse, data exfiltration, and trust-boundary failures
• Strengthen the safety envelope: signed scope, kill switch, non-destructive payloads, and full provenance for every action a model proposes
• Support private and sovereign deployments where models, GPUs, and evidence stay inside the customer's environment

Requirements:

• 4+ years in software engineering, with meaningful hands-on work building LLM-based systems, agents, or ML pipelines in production
• A real offensive security background, such as penetration testing, red teaming, exploit development, bug bounty, or security research. You know what a working exploit chain looks like
• Strong Python
• Experience with agent frameworks, tool calling, structured outputs, and prompt and evaluation design
• Working knowledge of at least a few of these: web/API exploitation, Active Directory and Entra attacks, cloud IAM and Kubernetes, network protocols, or container escape
• Comfort running open-weight models locally or on GPUs (vLLM, SGLang, or similar)
• The discipline to treat "the model said so" as a hypothesis, not a finding
• Strong Python. Go, Rust, or C is a plus
• OSCP, OSEP, OSWE, CRTO, or equivalent practical certifications
• Published CVEs, conference talks, or open-source offensive tooling
• Experience with AI red teaming or LLM security (OWASP LLM Top 10, MITRE ATLAS)
• Background in reinforcement learning, planning systems, or model fine-tuning
• Familiarity with OCSF, MITRE ATT&CK mapping, or compliance frameworks such as PCI DSS 4.0, NIST CSF 2.0, DORA, and NIS2