SDET Technical Lead (AI)

Location: Hybrid, with in-office presence at least three days per week. Office locations: Sunnyvale, CA or Toronto, ON. Fully remote work is not available.

Company

hirify.global builds large-scale AI accelerator hardware and software for high-speed model training and inference.

What you will do

• Establish and lead Release Integration Testing for AI Inference Core, including ownership boundaries, engagement criteria, and release gates.
• Define cross-stack E2E and regression strategies across runtime, host, device programming, memory, scheduling, model execution, infrastructure, and hardware.
• Review unit, simulation, benchmark, feature-test, and integration evidence to approve inference-path readiness.
• Improve master and release-branch stability through health metrics, failure classification, dashboards, qualification workflows, and release pipelines.
• Lead regression and rollout triage, drive root-cause analysis, and place missing coverage at the appropriate test layer.
• Mentor SDETs and partner with feature, infrastructure, integration, qualification, release, and deployment teams.

Requirements

• Strong software-engineering fundamentals and programming ability in Python, Go, or a similar language.
• Technical leadership experience 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 analyze ambiguous cross-stack failures, 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 multiple engineering teams and communicate technical risk during high-pressure release situations.

Nice to have

• Experience with hardware accelerators, compilers, kernels, runtimes, software/hardware co-design, or low-level systems.
• Experience with AI infrastructure, model deployment, LLMs, multimodal workloads, or large-scale compute clusters.
• Experience building distributed test systems, release pipelines, dashboards, developer tooling, or performance and reliability testing.
• Experience in a startup or resource-constrained engineering environment, including taking a quality or release capability from zero to one.
• Familiarity with containers, cluster orchestration, cloud infrastructure, CI/CD, or high-performance computing.

Culture & Benefits

• Work on a large-scale AI platform and custom accelerator architecture beyond GPU constraints.
• Opportunities to publish and open-source AI research.
• Access to one of the fastest AI supercomputers in the world.
• Startup vitality combined with job stability and a non-corporate work culture.
• Inclusive environment focused on continuous learning, growth, and support.