AI Inference Core - Senior SDET, Release Integration Testing

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. About the Role We are looking for an experienced, hands-on Senior Software Development Engineer in Test (SDET) to join the Release Integration Testing (RIT) function within Release & Feature Qualification for AI Inference Core. The Production Engine for Inference Core — turning integrated features into reliable production releases. In this role, you will develop sophisticated test automation, validate complex AI inference systems, investigate cross-stack failures, and drive improvements in release quality and reliability. You will work across AI frameworks, runtime, compiler, kernels, distributed systems, infrastructure, and hardware to identify integration risks and ensure production readiness. This is a highly technical, hands-on engineering role requiring strong software development, automation, and debugging skills. You will independently own complex testing initiatives, contribute to test architecture and release qualification strategies, and collaborate closely with engineering teams to improve system reliability. Release Integration Testing (RIT) is the bridge between feature qualification and release qualification. Feature teams retain ownership of feature design, feature-level qualification, and feature regression. RIT focuses on integration readiness, cross-stack validation, regression stability, and first-pass rollout triage for AI Inference Core. What Makes This Role Distinct Software engineering applied to quality: Design and develop scalable test automation, frameworks, diagnostics, and tooling to validate complex AI inference systems—not just execute test cases. Cross-stack technical challenges: Debug and validate interactions across AI frameworks, runtime, compiler, kernels, distributed systems, infrastructure, and hardware. Integration and release ownership: Independently drive integration testing, regression validation, and release qualification activities for complex inference-core features. Inference-path validation: Identify and test high-risk interactions across runtime, host, device programming, memory, scheduling, and model execution. Production reliability: Improve master and release-branch stability through robust automation, failure analysis, and actionable quality metrics. Technical collaboration: Partner with SDETs, software engineers, infrastructure teams, and release owners to resolve complex issues and improve engineering efficiency. What You Will Do 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. Minimum Skills & Qualifications Strong software engineering fundamentals with professional experience developing software, automated testing frameworks, or test infrastructure. Proficiency in Python, Go, or a similar programming language , 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. Preferred Skills 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. What Success Looks Like 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. Location This role follows a hybrid sche