Senior Software Development Engineer in Test - Verifiation And Validation

Responsibilities

• Lead the design and maintenance of large-scale event-driven software/hardware-in-the-loop (SIL/HIL) testing frameworks, utilizing advanced software patterns to improve modularity and reduce code duplication.
• Architect and manage end-to-end continuous integration (CI) pipelines to automate the execution of regression suites.
• Lead root cause analysis for complex system-level failures, developing log mining scripts and visualization dashboards (e.g., Grafana, SQL-based notebooks) to triage failures and track performance metrics.
• Perform gap analyses between high-level safety requirements and technical test implementations, including the development of intricate timing and prioritization patterns.
• Lead end-to-end validation and clearance process for safety-critical monitors, such as for collision detection systems.

Qualifications

• Good technical communication skills, with the ability to summarize complex test failures and provide clear impact assessments for our tests, tools, and pipelines.
• Demonstrated experience delivering complex projects in Python, including the use of AI-based coding, such as for infrastructure development, tooling and automated test writing.
• Deep understanding of HIL and SIL methodologies, including real-time fault injection.
• Ownership from design, to implementation and maintenance of automated testing pipelines (e.g., using Bamboo, Jenkins, or similar) and developing custom automation plugins.
• Practical knowledge of Linux internals and networking protocols used in high-throughput real-time systems.
• Bachelor's or higher degree in Mechatronics Engineering, Computer Science or a relevant technical field, such as Data Science or Electrical Engineering.
• 7+ years of experience in the above, with demonstrated ownership of 3+ complex projects.

Bonus Qualifications

• Strong C++.
• Pytest customization experience.
• Background in autonomous systems or safety-critical robotics validation.
• Experience applying Generative AI or Machine Learning tools to code, audit and improve test coverage and documentation.
• Experience with data engineering tools like SQL, PySpark, Looker or Databricks for large-scale performance analysis.
• Experience with real-time communication protocols (e.g., DDS, I2C, SPI, CAN, GMSL2).