AI Inference Core - SDET Technical Lead, Release Integration Testing

We are looking for a hands-on SDET Technical Lead to establish and lead Release Integration Testing (RIT) within Release & Feature Qualification for AI Inference Core.

The Production Engine for Inference Core - turning integrated features into reliable production releases.

You will define the quality strategy across the pre-release and release cycle, from feature and model integration through branch stability, release qualification, deployment, and post-release learning. You will work across AI frameworks, runtime, compiler, kernels, distributed systems, infrastructure, and hardware to make release risk visible and actionable.

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.

RIT is the bridge between feature qualification and release qualification. Feature teams retain ownership of feature design, feature-level qualification, and feature regression. RIT owns inference-core integration strategy, inference-path readiness approval, integrated cross-stack validation, and first-pass rollout triage., * Dedicated RIT ownership: Engage before feature qualification completes while keeping the boundary clear: feature teams own feature behavior and qualification; RIT 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.

What You Will Do

• 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., * 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.

• 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.

Preferred Skills

• 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.

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., 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.

Apply today and become part of the forefront of groundbreaking advancements in AI!

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.

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