Staff AI Security Engineer, CoreWeave

• 10+ years of experience in security engineering (cloud, application, or infrastructure)
• Experience with AI/ML systems: LLMs, training pipelines, inference systems, or MLOps
• Strong, demonstrable experience building and securing large-scale distributed systems
• Strong coding skills in Go, Python, or similar
• Familiarity with Kubernetes, containerized environments, and cloud platforms (AWS, GCP, Azure)
• Understanding of AI-specific threats: prompt injection, data leakage, model misuse, supply chain risks in models and datasets
• Track record of driving cross-team technical initiatives and influencing architecture decisions at the org level
• This position requires access to export controlled information. To conform to U.S. Government export regulations applicable to that information, applicant must either be (A) a U.S. person, defined as a (i) U.S. citizen or national, (ii) U.S. lawful permanent resident (green card holder), (iii) refugee under 8 U.S.C. § 1157, or (iv) asylee under 8 U.S.C. § 1158, (B) eligible to access the export controlled information without a required export authorization, or (C) eligible and reasonably likely to obtain the required export authorization from the applicable U.S. government agency

Desirable

• CoreWeave is seeking a Staff AI Security Engineer to define and operationalize security across our AI ecosystem. This role sits at the intersection of AI/ML, cloud infrastructure, and security engineering
• You'll build secure-by-default foundations for AI development, agentic workflows, and enterprise AI adoption, balancing velocity with strong security guarantees
• You will also partner closely with enterprise security and IT teams to manage AI-adjacent risks across the corporate environment, including shadow AI, data exfiltration through AI tooling, and SaaS security posture for AI-enabled platforms
• Design and implement security controls across AI/ML infrastructure, including model artifact storage, data lineage and integrity, model signing and provenance, and ML pipeline security (MLOps/MLSecOps)
• Develop AI security policies, standards, and threat models covering model development, training pipelines, data ingestion, inference environments, and agentic systems
• Build security guardrails for agentic workflows: tool access and permissioning, input/output validation, execution boundaries, sandboxing, and auditability of agent actions
• Lead secure adoption of AI tools across engineering, security, operations, and enterprise functions. Evaluate AI vendors, copilots, and integrations. Define policies for data sharing, model usage boundaries, and sensitive data handling in prompts and outputs
• Partner with enterprise security and IT on AI-related risks across the corporate environment, including endpoint posture, corporate identity, and SaaS security for AI-enabled platforms
• Conduct threat modeling and adversarial testing of AI systems, covering prompt injection, data poisoning, model extraction, and backdoored models. Build and maintain a CoreWeave-specific AI threat taxonomy
• Drive cross-team technical decisions and influence architecture across platform engineering, security engineering, ML/AI teams, and product/DevEx to embed security into AI platforms and developer tooling