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  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…