AI Inference Core - Senior SDET, Release Integration Testing

• Design, develop, and maintain automated test frameworks, tools, and infrastructure to validate AI inference software across multiple components and system layers.

• Independently own integration testing and qualification activities for complex inference-core features, identifying dependencies, interaction risks, and potential coverage gaps.

• Develop and execute comprehensive end-to-end, integration, regression, and system-level tests across AI frameworks, runtime, compiler, kernels, infrastructure, and hardware.

• Analyze unit, simulation, benchmark, feature-test, and integration results to assess readiness, identify gaps, and provide evidence-based recommendations for release qualification.

• Investigate and debug complex cross-stack failures, perform root cause analysis, collaborate with engineering teams on fixes, and implement automated tests to prevent regressions.

• Improve master and release-branch stability by identifying regressions, triaging failures, validating fixes, and contributing to release-quality metrics and reporting.

• Build and enhance internal tooling for test automation, diagnostics, monitoring, dashboards, CI/CD workflows, and release qualification pipelines.

• Collaborate with feature engineering, Integration, Core Infrastructure, and release teams to improve test coverage, accelerate debugging, and support coordinated feature rollouts.

• Contribute to test architecture, risk-based testing strategies, and continuous improvements in automation efficiency, system observability, and release reliability.

• Provide technical guidance and mentorship to junior engineers through code reviews, debugging sessions, and sharing testing best practices.

• Strong software engineering fundamentals with professional experience developing software, automated testing frameworks, or test infrastructure.

• Proficiency in

, with demonstrated experience writing maintainable, production-quality code.

• Experience designing, developing, and maintaining automated tests and frameworks for complex software systems.

• Strong debugging and problem-solving skills, including the ability to investigate failures across multiple components, analyze logs and system behavior, and identify root causes.

• Solid understanding of software testing methodologies, including integration testing, system testing, regression testing, and end-to-end validation.

• Experience working with CI/CD pipelines, test automation infrastructure, and software development workflows.

• Working knowledge of operating systems, networking, distributed systems, or systems-level software concepts.

• Ability to independently own complex technical problems, prioritize testing efforts, and drive issues through resolution.

• Strong communication and collaboration skills, with experience working across multiple engineering teams to deliver reliable software.

• Experience testing or developing distributed systems, infrastructure software, runtime systems, compilers, kernels, or other low-level software.

• Experience with AI/ML infrastructure, LLM inference, model deployment, multimodal workloads, or large-scale compute environments.

• Exposure to hardware accelerators, GPU systems, software/hardware integration, or high-performance computing.

• Experience building scalable test frameworks, distributed test systems, release pipelines, or internal developer tools.

• Familiarity with performance testing, profiling, observability, fault injection, reliability engineering, or production failure analysis.

• Experience with containers, Kubernetes, cloud infrastructure, cluster orchestration, or large-scale distributed computing environments.

• Experience using debugging and observability tools to diagnose complex system-level issues.

• Experience working in fast-paced engineering environments, including startups or teams developing complex systems software.

• You independently deliver robust, scalable automation and test infrastructure that improves integration and release qualification.

• Complex cross-stack defects are identified and resolved earlier in the development cycle, reducing issues discovered during final release qualification or production.

• Regression testing provides reliable, actionable signals that improve master and release-branch stability.

• Debugging cycles are shorter through improved diagnostics, automation, and effective collaboration across engineering teams.

• Test coverage and qualification processes evolve to support increasingly complex AI inference features and workloads.

• Your technical contributions improve release predictability, engineering efficiency, and overall system reliability.

• You are a trusted technical contributor who helps raise engineering quality through strong code, sound judgment, and mentorship.

• This role follows a hybrid schedule and requires in-office presence at least three days per week. Fully remote work is not available.

•

People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we've reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:

• Build a breakthrough AI platform beyond the constraints of the GPU.

• Publish and open source their cutting-edge AI research.

• Work on one of the fastest AI supercomputers in the world.

• Enjoy job stability with startup vitality.

• Our simple, non-corporate work culture that respects individual beliefs.

Find out more about what it's like to work at Cerebras