Product Security Engineer

Lead the design and implementation of AI-native security engineering for a frontier AI data lab. As a Product Security Engineer on the core team, you will operate where application security, machine learning, and developer productivity meet, building autonomous, AI-driven controls that discover risk, triage findings, and enable secure, high-velocity development. You will help secure AI agents, model-serving infrastructure, data pipelines, and customer-facing systems while scaling security as a native capability across the company.

The company is an AI data lab that converts domain expertise into high-quality training data, evaluations, and feedback loops to improve AI reasoning and performance. The platform vets global experts to scale expert contributions across domains such as finance, healthcare, and engineering, with the long-term aim of enabling many people to contribute expertise to AI.

• Architect and build AI-powered security systems that autonomously identify, triage, and remediate vulnerabilities across applications, infrastructure, and AI workloads.
• Develop agentic security workflows using large language models and machine learning for code review, threat detection, vulnerability correlation, root-cause analysis, and automated fix generation.
• Integrate intelligent security controls, AI guardrails, and continuous validation into CI/CD pipelines to evolve the Secure Software Development Lifecycle.
• Lead threat modeling for distributed systems, AI platforms, retrieval-augmented generation architectures, model-serving infrastructure, data pipelines, and autonomous agents.
• Design frameworks to protect AI systems from prompt injection, model abuse, data poisoning, adversarial attacks, and sensitive data leakage.
• Build behavioral detection models and risk engines to identify synthetic identities, document fraud, account takeover attempts, and other adversarial activity in customer onboarding and KYC workflows.
• Apply machine learning and contextual risk scoring to reduce alert fatigue, prioritize findings, and enable autonomous remediation decisions.
• Partner with engineering, platform, and AI research teams to embed security as a native capability.
• Scale a culture of security engineering through mentorship, technical leadership, and enablement focused on secure AI development practices.

• Demonstrated experience building or applying AI or LLM-powered security solutions, such as agentic workflows, autonomous remediation systems, vulnerability discovery tools, or security copilots.
• Deep expertise in Application Security, Product Security, or Security Engineering, with a strong software development background.
• Hands-on experience integrating enterprise security tooling into automated developer workflows and AI-driven orchestration platforms, examples include Snyk, Checkmarx, GitHub Advanced Security, Semgrep, Wiz, and Lacework.
• Strong understanding of modern security architecture, cloud-native systems, APIs, microservices, and distributed computing.
• Deep familiarity with OWASP Top 10, OWASP Top 10 for LLM Applications, secure AI development practices, and emerging AI threat models.
• Advanced programming skills in Python, plus proficiency in at least one additional language such as Go, Java, Rust, or Node.js.
• 8+ years of relevant experience.
• Required skills summary: AI and Security, Application Security Tooling, LLM Security, Programming Proficiency.

• Employment type: Full-time.
• Location: Remote.

Annual base compensation range: $250, 000 to $400, 000.

The source did not specify visa, sponsorship, or detailed work-authorization requirements. Applicants should ensure they are authorized to work remotely from their location.

The source did not include specific application instructions or steps. Candidates should prepare materials that demonstrate their experience with AI-driven security, relevant integrations with security tooling, and examples of prior technical leadership in security engineering.