Sr. QA Engineer

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.