Senior Software Engineer, Foundry

• 5+ years of experience in software or security engineering, with a focus on application and infrastructure security
• Knowledge of modern adversary tactics, techniques, and procedures
• Familiarity with security testing tools and methodologies (e.g., static code analysis, penetration testing)
• Expertise in Python and TypeScript, with experience securing production-level code
• Experience in cloud security, particularly with major cloud providers (AWS, Azure, GCP)
• Experience with zero-trust architectures, encryption protocols, and secure communication methods
• Knowledge of infrastructure as code tools like Terraform, focusing on secure configuration and compliance
• A passion for protecting AI and machine learning workloads, understanding the unique security requirements of GPU infrastructure.
• Exceptional problem-solving skills and the ability to anticipate vulnerabilities and build scalable, secure solutions
• Knowledge of infrastructure as code tools such as Terraform

Desirable

• Build and implement security strategies for AI workloads, including the secure handling of sensitive data and model artifacts
• Lead efforts to secure our distributed cloud infrastructure, focusing on containerized environments (Kubernetes), cloud-native security, and scaling across multiple regions
• Design and integrate security measures directly into our orchestration platform to protect customer workloads and ensure secure multi-tenant environments
• Implement zero-trust security architectures to protect data flows between distributed AI compute resources
• Secure cloud-based and on-premise GPU servers, ensuring compliance and data protection across regions and regulatory environments
• Lead penetration testing, threat modeling, and red team exercises to anticipate and defend against advanced persistent threats
• Identify and mitigate vulnerabilities in code (Python, TypeScript) and infrastructure, focusing on infrastructure as code (Terraform) and cloud services
• Establish security guidelines and best practices for AI-specific workloads, including data encryption, secure model storage, and access control for training and inference pipelines