Twitch Software Development Engineer I at Twitch Security Platform team responsible for designing, building, and operating security and privacy software services. Works on large-scale security telemetry and automates security workflows.
Responsibilities
Twitch Security Platform builds and operates the foundational software, data, and automation that enable security at scale across Twitch. As a Software Development Engineer I (SDE I) on the Security Platform team, you will design, build, and operate critical services, pipelines, and tooling that power Twitch's security, privacy, and compliance programs.
This role is ideal for an engineer who learns quickly, values operational excellence, and wants to see their work directly improve Twitch's security posture.
Work with technical program managers, technical leads, and other engineers to design, develop, launch, and operate security and privacy software
Work backwards from security and privacy problems to drive business impact
Maintain and improve our existing backend services.
Qualification
Bachelor's degree in Computer ScienceFamiliarity with TwitchFamiliarity with security
Required
Bachelor's degree in Computer Science, Engineering, or equivalent practical experience.
Experience developing software in a professional environment.
Fluency in a modern language (Go or similar).
Prior experience building internal tooling/products for other developers.
Preferred
Experience building software and services using AWS technologies such as ECS, DynamoDB, Lambda, SQS, and Step Functions
Experience with infrastructure-as-code (e.g., AWS CDK) or CI/CD pipelines.
Familiarity with Twitch, gaming, and/or streaming on Twitch
Familiarity with security, data, and privacy systems, such as security or audit data lakes, cloud authentication and authorization flows, vulnerability scanning and remediation pipelines, and privacy or compliance-driven workflows (e.g., CCPA, GDPR-related controls).
Experience working with large-scale event or log data, including event-driven architectures, asynchronous processing, or batch/streaming pipelines.
Experience integrating AI-assisted development or analysis tools into engineering workflows, such as using LLM-powered tooling, automation, or MCP-style platforms to improve developer productivity or system insight.
