Cerebras AI Inference Core - SDET Technical Lead responsible for establishing and leading Release Integration Testing for AI Inference Core. Define quality strategy across pre-release and release cycles.
Responsibilities
We are looking for a hands-on Technical Lead Manager (Release Qual & Integration) to establish and lead Release Integration Testing within Release & Feature Qualification for AI Inference Core.
The Production Engine for Inference Core — turning integrated features into reliable production releases.
This is a technical-leadership role, not a coordination-only position. You will design test architecture, lead difficult debugging and release decisions, mentor engineers, and write software and automation alongside the team.
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. Release Integration Testing owns inference-core integration strategy, inference-path readiness approval, integrated cross-stack validation, and first-pass rollout triage.
What Makes This Role Distinct
Dedicated Release Integration Testing ownership: Engage before feature qualification completes while keeping the boundary clear: feature teams own feature behavior and qualification; Release Integration Testing owns integration strategy, readiness approval, integrated validation, and first-pass rollout triage.
Inference-path readiness gate: Require evidence across unit, simulation, benchmark, feature, and integration testing, with explicit coverage gaps before release entry.
Cross-stack test strategy: Define risk-based E2E and regression coverage for features spanning components, organizations, software layers, infrastructure, and hardware.
Branch and rollout leadership: Establish measurable health standards for master and release branches, and coordinate inference-impacting rollout across multiple product and release projects.
Hands-on technical authority: Lead through code, test architecture, difficult debugging, quality metrics, and evidence-based release decisions.
Team multiplier: Raise the technical bar, mentor engineers, and align feature, infrastructure, integration, qualification, and release teams.
Define the Release Integration Testing strategy, engagement criteria, ownership boundaries, entry and exit criteria, coverage expectations, and escalation thresholds for AI Inference Core.
Qualification
Experience with AI infrastructureExperience building test frameworksExperience with performance testingFamiliarity with containers
Preferred
Experience with software/hardware co-design, hardware accelerators, compilers, kernels, runtimes, or low-level systems.
Experience with AI infrastructure, model deployment, LLMs, multimodal workloads, or large-scale compute clusters.
Experience building test frameworks, distributed test systems, release pipelines, dashboards, or internal developer tooling.
Experience with performance testing, profiling, observability, fault injection, reliability, or production failure analysis.
Experience managing geographically distributed teams across multiple regions and time zones is preferred
Experience in a startup or similarly fast-moving, resource-constrained engineering environment.
Track record of taking a quality or release capability from zero to one and scaling it across teams.
Familiarity with containers, cluster orchestration, cloud infrastructure, CI/CD, or high-performance computing.
