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

About the role

What you will do

• Set testing strategy, ownership boundaries, readiness criteria, coverage expectations, and escalation thresholds.
• Identify cross-stack dependencies and risks across runtime, host, device programming, memory, scheduling, models, infrastructure, and hardware.
• Review unit, simulation, benchmark, feature-test, and integration evidence; document coverage gaps and approve release-entry readiness.
• Lead integrated inference end-to-end validation and develop risk-based release regression scenarios.
• Improve branch health through metrics, failure classification, reports, dashboards, workflows, and release pipelines.
• Lead regression and rollout triage, root-cause analysis, issue resolution, coverage placement, and rollout planning.
• Partner with and mentor engineering, infrastructure, qualification, release, and deployment teams; improve automation and diagnostics.

Minimum qualifications

• Strong software-engineering fundamentals and programming ability in Python, Go, or a similar language.
• 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.
• Ability to investigate ambiguous cross-stack failures and drive evidence-based resolution.
• Strong understanding of risk-based testing, release readiness, regression strategy, failure analysis, and quality metrics.
• Ability to align engineering teams and communicate technical risk clearly, including under release pressure.

Preferred experience

• Software/hardware co-design, accelerators, compilers, kernels, runtimes, or low-level systems.
• AI infrastructure, model deployment, LLMs, multimodal workloads, or large-scale compute clusters.
• Test frameworks, distributed test systems, release pipelines, dashboards, or developer tooling.
• Performance testing, profiling, observability, fault injection, reliability, or production failure analysis.
• Distributed-team management, startup experience, scaling quality capabilities, containers, orchestration, cloud infrastructure, CI/CD, or HPC.

Success measures

• Release readiness relies on explicit criteria and high-signal evidence.
• Fewer integration defects are first discovered in final qualification or production.
• Cross-component risks are found earlier, debugging is faster, and coverage ownership is clear.
• Branch health and release metrics are measurable and drive predictable decisions and rollouts.

Location and office policy

• The role requires in-office presence at least three days per week; fully remote work is unavailable.
• Office locations are Sunnyvale, California, or Toronto, Ontario.