Staff Software Development Engineer in Test, Cloud Applications (Remote)

Brain Corp is a San Diego-based AI company creating transformative core technology for the robotics industry. They are seeking a Staff Software Development Engineer in Test to lead the design and evolution of quality engineering architecture across their web applications, cloud services, mobile platforms, and data systems.

Responsibilities:

• Design and implement scalable automation frameworks and validation platforms using Python
• Build reusable automation libraries and developer-facing tooling that enable teams to validate functionality earlier in the development cycle
• Architect automated validation strategies across: Modern web applications, APIs and microservices, Distributed cloud systems, Mobile platforms, Data pipelines and analytics platforms including ML outputs
• Integrate automation frameworks into CI/CD pipelines to enable rapid feedback and high-confidence releases
• Architect automated testing strategies for mobile applications across iOS and Android platforms
• Leverage cloud device platforms such as BrowserStack to enable scalable cross-device testing
• Integrate mobile automation into CI/CD pipelines to support validation across devices, operating systems, and configurations
• Define strategies for mobile UI testing, API validation, and end-to-end workflow testing
• Design validation strategies for cloud-native microservices architectures
• Enable automated testing for service interactions through contract testing, integration validation, and environment simulation
• Partner with infrastructure and DevOps teams to embed quality gates within deployment pipelines
• Drive improvements in system reliability, performance, and resilience testing
• Architect automated validation for data pipelines, data transformations, and data platform services
• Establish frameworks to validate data integrity, schema evolution, lineage, and reproducibility
• Partner with Data Engineering teams to ensure quality is built into analytics and machine learning
• Partner with data science and ML Ops teams to ensure reproducibility, monitoring, and validation of ML models within production systems, including drift detection and model performance validation
• Act as a technical leader across engineering teams, influencing architecture and engineering practices to improve product quality and system reliability
• Collaborate with product management, development, and DevOps teams to align testing strategies with product goals
• Facilitate the adoption of AI/ML/LLM-enabled testing strategies where appropriate
• Contribute to system and design reviews to ensure new components are observable, testable, and resilient
• Serve as a working lead and manager for a team of SDETs, providing architectural guidance and mentorship
• Conduct performance reviews, support career development, and foster engineering excellence within the team
• Guide the team’s technical direction while remaining hands-on in framework design and automation architecture
• Coordinate quality engineering initiatives across projects to ensure alignment with organizational priorities
• Establish metrics, dashboards, and observability strategies that provide real-time visibility into product health and release readiness
• Provide engineering leadership with data-driven insights into system reliability, defect trends, and risk areas
• Investigate system behaviors and diagnostic data to identify root causes and drive cross-team issue resolution
• Ensure traceability between requirements, implementation, and verification

Requirements:

• Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent practical experience
• 7+ years of experience in software engineering or SDET roles working with complex distributed systems, with at least 3 years in a technical leadership role
• Strong software development skills in Python, including building automation, tooling, or validation systems
• Demonstrated experience designing and implementing automation frameworks or validation platforms across multiple layers of the stack (UI, API, service, integration, and data)
• Strong experience validating web applications, APIs, and distributed microservices architectures
• Demonstrated ability to integrate automated validation strategies into CI/CD pipelines
• Strong understanding of modern software testing methodologies and quality engineering practices, including functional, integration, and system testing
• Experience implementing browser automation frameworks such as Playwright or Cypress
• Strong debugging and analytical skills, including the ability to investigate complex failures and support root cause analysis
• Demonstrated ability to mentor engineers and influence engineering practices within a team or product area
• Excellent communication, collaboration, and technical documentation skills
• Proven capability in utilizing AI/ML-assisted testing techniques to enhance coverage, diagnostics, and defect detection
• Experience validating data platforms and data pipelines, including SQL-based systems, data warehouses, and ETL/ELT workflows
• Experience implementing contract testing or service-level validation strategies in distributed systems
• Experience building or improving quality dashboards, metrics, or reporting systems used to evaluate system health and release readiness
• Experience testing mobile applications and working with cross-device testing platforms