Founding Security Engineer

Create goal-oriented AI agents capable of executing multi-step offensive security workflows to help our customers identify, prioritize, and remediate vulnerabilities. Build and optimize a cloud-native framework that leverages AWS services to support AI-powered offensive security workflows at scale. Integrate cutting-edge LLM models with cybersecurity tools and datasets to simulate traditional human-driven workflows. Continuously refine and expand the capabilities of Specular to help our customers stay ahead of e-crime and nation state cyber attacks. Collaborate closely with our customers to understand their unique security challenges, gather feedback on existing tools, and identify opportunities for improvement. Required Experience Offensive Experience: 3 years of experience in offensive security, red teaming, or penetration testing. Software Engineering: Proficient in Python, Bash, PowerShell, and AWS services (ECR/ECS, Lambda, S3, etc) to automate attacks. Red Teaming / Penetration Testing Experience: Expertise in simulating e-crime or advanced persistent threat (APT) scenarios to test enterprise security defenses. Attack Simulation (External): Experience analyzing and exploiting organization network perimeters with a focus on web applications, cloud, and other common enterprise applications. Attack Simulation (Internal): Experience with reconnaissance, privilege escalation, lateral movement, and exfiltration within Active Directory, Cloud (AWS/Azure/GCP), and hybrid networks. Vulnerability Analysis: Experience with traditional vulnerability scanners and tools like NMAP, Nessus, Qualys, Nuclei, Burpsuite, etc. Vulnerability Exploitation: Familiarity with identifying and exploiting a wide array of vulnerabilities across different operating systems and architectures. Reporting and Communication: Skilled in writing findings and recommendations to convey technical concepts to senior leadership and non-security personnel. Bonus Points Experience Prompt Engineering: Skilled in crafting effective prompts to optimize the performance of language models and enhance AI-driven applications. Large Language Models (LLM) Frameworks: Practical experience with at least one of the popular frameworks such as: LangChain : For building applications with large language models. LlamaIndex : For developing and deploying language models. AutoGen : For creating agentic workflows. API Development: Expertise in designing and implementing RESTful APIs to facilitate communication between different components of the AI system. Compliance Frameworks: Familiarity with offensive security operations in the context of compliance frameworks like PCI-DSS, HIPAA, and SOC 2. Compensation The base salary range for this position in the U.S. is $150,000 - $225,000 per year equity benefits. J-18808-Ljbffr