AI Inference Core - Technical Lead Manager, Release Qual & Integration

The role

You will establish and lead Release Integration Testing within Release & Feature Qualification for AI Inference Core. You will define quality strategy, design test architecture, lead debugging, and mentor engineers across the AI frameworks, runtime, compiler, and hardware stack.

What you will do

• Define Release Integration Testing strategy, engagement criteria, and ownership boundaries
• Identify dependencies and interaction risks across the software and hardware stack
• Own the inference-path readiness gate by reviewing testing evidence
• Lead integrated inference E2E validation across features and the cloud-to-wafer stack
• Improve master and release-branch stability through health metrics and reporting
• Lead first-pass regression and rollout triage
• Partner with and mentor SDETs, feature teams, and deployment teams
• Advance automation efficiency, diagnostics, and roadmap test planning

Required qualifications

• Strong software-engineering fundamentals and programming ability in Python 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

Nice to have

• 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
• 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

Benefits

• Build a breakthrough AI platform beyond the constraints of the GPU
• Publish and open source cutting-edge AI research
• Work on one of the fastest AI supercomputers in the world
• Job stability with startup vitality
• Simple, non-corporate work culture

About the company

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. The company works with leading model labs, global enterprises, and cutting-edge AI-native startups.