About the role
The Senior QA Engineer will work closely with senior engineers and QA leadership to support performance benchmarking, reliability testing, and automation across our distributed storage ecosystem.
Responsibilities:
- Build, automate, and execute performance, stress, scale, and regression tests for distributed clustered storage systems.
- Develop automated test suites to validate storage resilience, data integrity, low-latency performance, and throughput across bare-metal and Kubernetes environments.
- Write test automation scripts and benchmarking tools using Python and modern scripting languages.
- Contribute to benchmark suites tailored to AI workloads, modern AI models, and inference engines to measure storage performance under real-world AI traffic.
- Collaborate with systems and platform engineers to analyze test results, isolate performance bottlenecks, and support system stability validation.
- Use AI-assisted tools such as GitHub Copilot and automated test generation tools to improve efficiency in day-to-day testing workflows.
- Participate in the team's QA practices and standards, collaborating with engineers across all functions.
Requirements:
- Bachelor's or master's degree in computer science, computer engineering, electrical engineering, or a related technical field, or equivalent practical experience.
- 5 to 7 years of hands-on professional experience in software quality assurance, performance testing, or test automation for enterprise or distributed systems.
- Exposure to testing in multi-node or clustered environments.
- Solid foundation in computer science fundamentals, including data structures, algorithms, operating systems, and basic computer architecture.
- Hands-on experience designing, automating, or running performance, load, stress, or regression tests for distributed systems.
- Experience with QA automation in bare-metal or Kubernetes environments; comfort with at least one is expected, and exposure to both is a plus.
- Good programming skills in Python, along with working knowledge of Linux scripting.
- Basic understanding of AI models and inference engines, or a strong interest in learning benchmarking and evaluation methodologies for inference workflows.
- Familiarity with modern development and QA toolchains, including AI-assisted development tools such as VS Code, GitHub, and Copilot.
- Strong analytical, debugging, and problem-solving skills with good attention to detail.
- Ability to adapt and contribute in a fast-paced, collaborative startup environment.
- Genuine interest in developing QA and benchmarking expertise over time while collaborating with engineers across functional areas.
- Eagerness to use AI tools to automate testing and increase development velocity.
Good to Have:
- Exposure to infrastructure-as-code tools such as Terraform on AWS or GCP.
- Familiarity with performance analysis or profiling tools such as SystemTap, Valgrind, and system metrics collection.