Cerebras Systems - AI Inference Core - SDET Technical Lead, Release Integration Testing
Requirements
• Strong software-engineering fundamentals and programming ability in Python, C++, Go, or a similar language. • Demonstrated technical leadership in software quality, test infrastructure, systems validation, release engineering, or complex software integration. • Experience designing automation and test architecture for distributed, systems-level, infrastructure, or AI software. • Proven ability to break down ambiguous cross-stack failures, form hypotheses, gather evidence, and drive issues to resolution. • Strong understanding of risk-based testing, release readiness, regression strategy, failure analysis, and quality metrics. • Ability to influence and align multiple engineering teams without relying solely on organizational authority. • Clear communication and sound judgment during high-pressure release situations, including the ability to explain technical risk to engineering and leadership audiences. • 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 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. • What Success Looks Like • Release readiness is based on explicit criteria and high-signal evidence rather than intuition. • Fewer inference-path integration defects are first discovered in final release qualification or production. • Cross-component risks are found earlier, debug cycles are shorter, and coverage ownership is explicit. • Master and release-branch health is measurable, actionable, and steadily improving. • Test automation and release infrastructure shorten feedback loops without sacrificing signal quality. • Release metrics and reports drive clear decisions, ownership, and predictable feature rollout. • Engineers across the organization are more effective because RIT provides strong technical direction, tooling, and mentorship. • Location • Location • This role follows a hybrid schedule and requires in-office presence three days per week. Fully remote work is not available. • Office locations: Sunnyvale, CA or Toronto, ON.
Responsibilities
• Define the RIT strategy, engagement criteria, ownership boundaries, entry and exit criteria, coverage expectations, and escalation thresholds for AI Inference Core. • Engage early on high-risk inference changes; identify dependencies and interaction risks across runtime, host, device programming, memory, scheduling, model execution, infrastructure, and hardware. • Own the inference-path readiness gate by reviewing unit, simulation, benchmark, feature-test, and integration evidence, documenting gaps, and approving integration readiness before release entry. • Lead integrated inference E2E validation across features and the cloud-to-wafer stack; promote durable feature tests and add risk-based scenarios to release regression. • Improve master and release-branch stability through actionable health metrics, failure classification, release-quality reporting, dashboards, qualification workflows, and release pipelines. • Lead first-pass regression and rollout triage, coordinate owners through resolution, drive RCA, place missing coverage at the correct layer, and plan rollout across multiple product and release projects. • Partner with and mentor SDETs, feature teams, Integration, Core Infra, release owners, and deployment teams; between active engagements, advance automation efficiency, diagnostics, probes, and roadmap test planning.
Benefits
• 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 here!
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