AI Inference Core - Senior SDET, Release Integration Testing

Cerebras Systems•Sunnyvale, CA

21d•Hybrid

About The Position

Cerebras Systems is seeking a 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. RIT bridges feature qualification and release qualification, focusing on integration readiness, cross-stack validation, regression stability, and first-pass rollout triage. Feature teams retain ownership of feature design, feature-level qualification, and feature regression. The role applies software engineering to quality: developing scalable test automation, frameworks, diagnostics, and tooling to validate complex AI inference systems. The SDET will work across AI frameworks, runtime, compiler, kernels, distributed systems, infrastructure, and hardware to identify integration risks and ensure production readiness. The position involves independently owning complex testing initiatives, contributing to test architecture and release qualification strategies, and collaborating with engineering teams to improve system reliability. The position follows a hybrid schedule with in-office presence at least three days per week. Fully remote work is not available. Office locations are Sunnyvale, California, or Toronto, Ontario.

Requirements

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 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 investigating failures across components, analyzing logs and system behavior, and identifying root causes.
Solid understanding of integration, system, regression, and end-to-end testing methodologies.
Experience 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, including experience working across multiple engineering teams.

Nice To Haves

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

Responsibilities

Design, develop, and maintain automated test frameworks, tools, and infrastructure to validate AI inference software across components and system layers.
Own integration testing and qualification activities for complex inference-core features, identifying dependencies, interaction risks, and coverage gaps.
Develop and execute 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 release qualification recommendations.
Investigate and debug cross-stack failures, perform root cause analysis, collaborate 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 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.