Build automated testing frameworks and stress-testing suites to validate the reliability and performance of AI infrastructure across distributed training, inference, GPU kernels, and system boundaries.
Software Development Engineer in Test
๐ Description
- Design, develop, and maintain large-scale event-driven SIL/HIL tests and modular testing frameworks.
- Build and manage CI pipelines that automate regression test execution.
- Investigate complex system-level failures using log-mining scripts and dashboards to triage issues and track performance.
- Analyze gaps between safety requirements and test implementations, including timing and prioritization behavior.
- Validate and clear safety-critical monitors, including collision detection systems.
๐ ๏ธ Requirements
- Bachelor's or higher degree in Mechatronics Engineering, Computer Science, Electrical Engineering or a relevant technical field.
- 4+ years of experience with demonstrated ownership of complex projects
- 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, Buildkite, Jenkins, or similar CI Tools) and developing custom automation plugins.
- Practical knowledge of Linux internals and networking protocols used in high-throughput real-time systems.
- 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.
Full job description
In this role, you will:
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:
Bachelor's or higher degree in Mechatronics Engineering, Computer Science, Electrical Engineering or a relevant technical field.
4+ years of experience with demonstrated ownership of complex projects
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, Buildkite, Jenkins, or similar CI Tools) and developing custom automation plugins.
Practical knowledge of Linux internals and networking protocols used in high-throughput real-time systems.
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 real-time communication protocols (e.g., DDS, I2C, SPI, CAN, GMSL2).
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The Software Engineer in Test will design and develop automated testing solutions, maintain testing infrastructure, and collaborate with teams to ensure software quality.
Develops and maintains automated tests, frameworks, and quality reporting across the software lifecycle, partnering with Agile engineering and DevOps teams to improve test coverage, prevent defects, and support reliable releases.
Build automation frameworks and stress-testing suites to validate the reliability and performance of AI infrastructure across distributed systems, GPU kernels, and AI frameworks.
Build automation frameworks and stress-testing suites to validate the reliability and performance of AI infrastructure across distributed systems, GPU kernels, and AI training and inference workloads.
