Senior Security Engineer, Incident Response

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.

We are hiring a Senior Security Engineer, dedicated to Product Security Incident Response. In this role, you will lead and architect Snowflake's product-integrated Incident Response strategy, with a primary focus on AI and LLM security. You'll design, plan, and drive the implementation of incident response capabilities across Snowflake's AI product surface - including Cortex AI, Cortex Agents, Snowflake Intelligence, and the data pipelines that power them.

• Lead incident response for product-level security events, with deep focus on AI-specific threat vectors including prompt injection, model abuse, agent hijacking, and data exfiltration through AI workloads.

• Integrate IR into AI product pipelines - work directly with teams shipping Cortex features, Snowflake Intelligence, and AI-powered developer experiences to embed security requirements from design through deployment.

• Develop and codify our AI abuse response strategy - defining detection, containment, and remediation playbooks for LLM misuse, adversarial inputs, and AI-assisted attacks targeting Snowflake customers.

• Address tech debt across the AI product stack, ensuring that new Cortex and agentic architectures meet IR readiness requirements from the ground up.

• Represent the IR team to cloud engineering, AI platform teams, corporate security, and customer-facing business units.

• Secure modern AI-native codebases operating across multi-cloud environments - including container-based inference services, RAG pipelines, vector stores, and agent orchestration layers.

• Partner with world-class AI and security engineering teams, providing expert guidance on secure architecture for high-impact AI features and customer-facing AI capabilities.

• Design and manage response capabilities built into Snowflake's AI operational infrastructure - from model serving endpoints to Cortex Search indexes and Snowpark ML pipelines.

• Lead with data, code, and automation - build tooling that accelerates detection and response for product security incidents at Snowflake scale.

• Drive meaningful security outcomes for the customers and enterprises trusting Snowflake with their most sensitive data and AI workloads.

• 5+ years of experience in information security, primarily in incident response, security engineering, or product/application security (preferred).

• Direct experience serving as incident commander for product focused security incidents.

• Experience leading or actively building an application or security engineering program, with a clear point of view on securing AI/ML systems.

• Experience with threat modeling and security testing across AI attack surfaces, including prompt injection, indirect injection, model inversion, embedding extraction, and supply chain attacks on AI dependencies.

• Familiarity with the unique data governance and security challenges introduced by LLMs, RAG architectures, and agentic systems.

• Working knowledge of cloud-native environments (AWS, Azure, GCP) and the threat landscape specific to SaaS and AI platforms.

• SQL proficiency, plus experience building automation and tools with common programming languages (Python preferred).

• Strong communication skills, with the ability to translate security risk into actionable guidance for product teams.

• Empathy for developer experience, helping AI engineers ship securely rather than slowing them down.

• Bachelor's degree in Computer Science or a related field, or equivalent experience.

• Experience securing AI/ML infrastructure, including model serving, vector databases, embedding pipelines, API gateways, and LLM-integrated application architectures.

• Experience building agentic incident response capabilities, including skills, agents, and pipelines.

• Understanding of current attacker TTPs, including emerging AI-specific techniques such as adversarial ML, agent manipulation, and LLM jailbreaking in enterprise contexts.

• Familiarity with CI/CD and secure release lifecycle patterns, with an emphasis on building security into AI feature pipelines.

• Preferred certifications: GCIA, GCIH, GCSA, GDAT, CISSP/GISP, or cloud certifications (AWS, Azure, GCP).