Senior Quality Assurance Engineer

Join Hayden AI as a Senior Quality Assurance Engineer, where you will drive quality through well-defined testing processes and disciplined release management for embedded software in real-world transportation environments. You will be responsible for establishing and executing manual and automated test strategies, managing regression cycles, and ensuring release readiness across embedded software versions. You will collaborate closely with Software Engineers, Product Managers, and Operations teams to embed quality into the development lifecycle and support reliable field deployments.

• Establishing and executing manual and automated test strategies, defining quality gates, managing regression cycles, and ensuring release readiness across embedded software versions.
• Owning the end-to-end quality process for embedded software releases, including test planning, risk assessment, defect triage, release sign-off, and post-release validation.
• Collaborating with Embedded Software Engineers during the design phase to advocate for testability, reliability, and expanded automation coverage.

• Experience: Minimum of 7 years in Quality Assurance, Test Engineering, or SDET roles, with a focus on structured testing and software validation
• Personal Attributes: [Communication, collaboration, adaptability, problem-solving attributes]
• Familiarity with video pipelines, streaming systems, or sensor-driven workflows
• Strong communication and cross-functional collaboration skills
• Experience with test management, defect tracking, and release processes
• Experience with system-level, integration, and end-to-end automation
• Experience testing devices, embedded systems, or IoT platforms
• Solid understanding of SDLC, CI/CD pipelines, and automation-first QA methodologies
• Education: Bachelor’s degree in Computer Science, Engineering, or equivalent experience
• Strong proficiency in Python for building and maintaining automated test frameworks
• Familiarity with backend services and distributed systems to support system-level and integration testing
• Experience testing AI/ML-driven systems, including model validation, data quality checks, and performance monitoring in production environments
• Hands-on experience with observability and reporting tools such as Grafana, Datadog, or similar platforms for quality metrics, monitoring, and release health reporting