About the role:
As a Senior Software Development Engineer in Test I (Senior SDET I), you will design and implement robust testing strategies and automation solutions for complex, cross-team product features. You will own the end-to-end automation roadmap for one or more modules defining strategic goals, execution plans, and success metrics.
The SDET team plays an important role in our company’s product development process. You’ll collaborate closely with engineers, TPMs, and product leaders to ensure our quality efforts scale with our product complexity. You will influence testing best practices across teams, driving improvements in automation coverage, performance, reliability, and AI-assisted quality engineering capabilities.
This is a highly technical and hands-on role that requires deep experience in automation, a strategic mindset, and the ability to thrive in a dynamic, cross-functional environment. We are particularly looking for engineers who are excited about the next generation of AI-first testing, intelligent automation, and agentic QA systems.
If you join Blink, you will:
- Design and implement end-to-end automation frameworks and testing strategies for complex, cross-team features.
- Own the automation roadmap for one or more modules, defining goals, execution plans, and success metrics.
- Anticipate risks, identify testability gaps, and drive continuous improvements in quality and efficiency.
- Build and maintain functional, performance, resiliency, and security automation tools integrated into CI/CD pipelines.
- Design scalable test infrastructure for distributed systems, event-driven architectures, and cloud-native platforms.
- Collaborate across engineering, product, platform, and QA teams to ensure comprehensive coverage and reliable releases.
- Leverage AI-assisted automation tooling to accelerate test creation, maintenance, debugging, root-cause analysis, and release validation.
- Build AI-first automation workflows using LLM-powered tooling, intelligent test generation, self-healing automation, and agentic QA approaches.
- Drive innovation in autonomous quality engineering systems, including AI-assisted CI/CD validation, failure triaging, and predictive quality analysis.
- Champion modern quality engineering practices including observability-driven testing, production validation, synthetic monitoring, and intelligent automation standards.
- Provide technical mentorship to SDETs and engineers, elevating automation design, AI-assisted engineering practices, and code quality across teams.
About You:
We all help each other on the team. Where you have previously worked or gone to school isn’t important to us.
The following, however, is:
- You have at least 5+ years of experience in SDET contributions, automation engineering, and quality tooling.
- You are passionate about delivering high-quality, reliable products through automation and intelligent testing strategies for both frontend and backend systems.
- You have advanced proficiency in testing methodologies, automation frameworks, debugging techniques, and distributed system validation.
- You are experienced in building and maintaining test infrastructure across multiple platforms and services.
- You have strong command over algorithms, data structures, databases, and scripting languages (SQL, Python, JavaScript, TypeScript, or Java).
- You have hands-on experience with CI/CD pipelines, performance engineering tools, observability platforms, and monitoring systems.
- You have deep expertise in API and backend testing, ideally with experience in AWS and distributed systems such as Kafka, Kinesis, or event-driven architectures.
- You have hands-on experience setting up performance/load/stress testing infrastructure at scale.
- You have practical experience using AI-assisted development and automation tools to improve test coverage, productivity, and release confidence.
- You understand modern AI-first QA concepts including intelligent test generation, self-healing automation, autonomous validation workflows, and agentic QA systems.
- You are comfortable evaluating and integrating emerging AI tooling into engineering and quality workflows.
- You have strong systems thinking and can identify opportunities where AI can improve reliability, observability, and engineering efficiency.
Extras:
- Experience with cloud-native QA automation architecture and tool implementation.
- Working knowledge of event-driven systems and distributed test environments.
- Familiarity with observability and monitoring tools for debugging in production.
- Experience with AI-powered testing platforms, autonomous QA agents, or LLM-assisted engineering workflows.
- Exposure to AI-assisted CI/CD validation, synthetic monitoring, chaos engineering, or intelligent release governance systems.
- Experience building scalable quality platforms for microservices and workflow orchestration systems.