MINIMUM QUALIFICATIONS

• 8–12 years in QA/Test for systems software or platform engineering, with at least 4 years focused on GPU software, device drivers, or firmware validation.
• Demonstrable ownership of validation for AI/ML pipelines and serving stacks using PyTorch and at least one modern inference framework (e.g., vLLM), including accuracy baselining and performance regression detection.

Proven expertise testing drivers and firmware with hands-on work in

• PCIe fundamentals (link training, BARs, MSI/MSI-X), DMA engines, interrupt handling, and memory models.

Failure modes: error injection, recovery paths, power/thermal events, and persistence across reboot/upgrade cycles.

Deep proficiency in Linux (kernel/user space) and practical experience with Windows driver ecosystems; ability to

• Read kernel logs and symbols, trace with ftrace/perf/ETW, and perform cross-layer debugging.
• Build custom kernels/modules and analyze crash dumps (kdump, WinDbg).

Strong programming for test automation

• Python for framework and orchestration (pytest or equivalent), robust mocking/fixtures, and data-driven test generation.
• C/C++ for low-level test harnesses, protocol exercisers, and performance micro-benchmarks.
• Bash/PowerShell for environment setup, CI scripting, and reproducibility.

CI/CD mastery with GitHub Actions/Workflows and/or Jenkins

• Design gated pipelines with parallelization, artifact management, flaky test quarantine, and automated rollback criteria.
• Integrate metrics, alerts, and quality reports; enforce go/no-go release thresholds.

Performance testing rigor

• Methodology for baselining, variance control, and noise isolation; application of statistical techniques (e.g., confidence intervals, A/B comparisons) to detect regressions.
• GPU-focused profiling and analysis (e.g., perf counters, memory bandwidth, kernel occupancy).

Tooling fluency

• gdb, perf, ftrace, valgrind, WinDbg, ETW; log/trace correlation; containerized test environments (Docker) and familiarity with Kubernetes for distributed tests.

Exploratory testing mindset

• Hypothesis-driven investigation, boundary and adversarial testing, fuzzing (protocol/API), chaos/fault injection, and reverse-engineering of interfaces when documentation is limited.

Communication and leadership

• Clear, concise defect reporting; ability to drive triage across teams; establish and evangelize quality standards; maintain strong documentation discipline.

GOOD TO HAVE

Lab ops for QA: rack mounting, server configuration, BMC/IPMI, BIOS/fw updates, network/storage setup, power/thermal profiling.

Front-end/UI testing experience for internal tools: ReactJS, web UI automation, accessibility and usability checks.

Backend/DB validation: REST/gRPC testing, SQL/NoSQL, schema migrations, data integrity, performance tuning.

Observability: Prometheus/Grafana, OpenTelemetry; integrating quality signals and alerts into CI/CD and release gates.

EDUCATIONAL QUALIFICATIONS

• BS/MS in Computer Science/Computer Engineering, or related discipline.
• #LI-NR1

Benefits offered are described: AMD benefits at a glance. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.