Data Security Engineer, Medal.tv

• Strong experience with cloud infrastructure (especially GCP), Terraform, Kubernetes, containers, and modern CI/CD processes
• Deep security fundamentals: IAM design, network segmentation, least privilege, secure-by-default patterns, and threat modeling
• Hands-on experience with GCP/AWS security stacks (IAM, VPC SC, KMS, workload identity, Secrets Manager)
• Familiarity with infrastructure observability and logging systems (Prometheus, Grafana, Cloud Logging, SIEM)
• Strong secrets management and PKI experience (Vault or cloud-native equivalents)
• Experience implementing or operating red-team / purple-team exercises
• Understanding of secure coding practices and modern software supply-chain risks
• Practical experience defending high-velocity environments with complex CI/CD workflows

Desirable

• General Intuition and Medal operate at the intersection of large-scale gaming data, AI research, and high-velocity product engineering
• This role secures the infrastructure that bridges these worlds
• You will harden our cloud environments, protect our data pipelines, and ensure our deployment systems are safe from supply-chain attacks and other threats
• You’ll design secure-by-default foundations without slowing down research or product teams, blending off-the-shelf security tooling with custom guardrails where necessary
• Your work directly reduces operational risk across both General Intuition and Medal, and ensures we maintain trust as we scale sensitive AI workflows and massive creator-driven platforms
• Reduce the attack surface of GCP workloads, Kubernetes clusters, and on-prem systems through hardened defaults, workload isolation, and IAM discipline
• Build automated security guardrails directly into Terraform, CI/CD, and Kubernetes deployments
• Maintain comprehensive observability and auditability across all infrastructure components
• Harden ingestion and ETL systems used for video and metadata processing
• Enforce encryption standards, strict IAM scopes, audit logging, and boundary isolation between pipelines and production systems
• Ensure complete visibility into the transfer and use of AI training data
• Build visibility into privileged access and access drift across engineering
• Automate key rotation, scoped roles, certificate workflows, and least-privilege baselines
• Maintain a full audit of secrets and enforce secure handling across endpoints, services, and pipelines
• Protect build systems against supply-chain attacks
• Ensure all builds and dependencies are scanned and recorded with a verifiable audit trail
• Strengthen GitHub Actions runners, workload identities, and artifact provenance
• Develop and run drills, tabletop exercises, and incident-response workflows
• Coordinate and validate external penetration tests
• Conduct internal red-team tests and support threat modeling with engineering
• Ensure we meet compliance requirements relevant to both creator data and AI training data