Senior Quality Assurance Engineer

Cerebras Systems•Sunnyvale, CA

7d•$220,000 - $240,000•Remote

About The Position

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

Requirements

Master’s degree or foreign equivalent degree in Computer Engineering, Computer Science, or a related field and 4 years of experience as Quality Engineer, Software QA Engineer, Sr. Software QA Engineer, Senior Quality Assurance Engineer or a related occupation required.
Java and QE Automation
Selenium testing with Page Object Model-based frameworks
Visual Studio Code
Rally
REST API Testing
AWS, Jenkins, and CI/CD pipeline
SQL
JIRA
JMeter

Nice To Haves

Python
Oracle/RDBMS
Kubernetes

Responsibilities

Design and develop automated test frameworks and execute software validation using Python, Java, and Selenium to ensure quality and reliability of cloud-based inference services.
Design, implement, and execute functional, integration, regression, and performance test strategies for AI/ML systems using Python, REST APIs and automation tools within CI/CD pipelines.
Develop and maintain automated test scripts and frameworks using Selenium, Java, and Python integrated with Jenkins and cloud environments such as AWS and Kubernetes to validate scalable SaaS deployments.
Perform system, API, and data validation testing using SQL, Oracle/RDBMS, and REST API tools to ensure data integrity, transformation accuracy, and end-to-end pipeline reliability.
Monitor system performance, latency, and model behavior using tools such as JMeter and observability platforms: analyze results to ensure optimal performance of distributed AI systems.
Triage defects, perform root cause analysis, and debug complex issues across distributed cloud systems and AI inference infrastructure using logs, monitoring tools, and engineering best practices.
Document test plans, test cases, results, and defects. Track issues using Jira or Rally and collaborate with cross-functional teams to ensure high-quality product releases.