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THE ROLEWe are seeking an experienced engineer to validate and debug data center GPU products across the complete software and hardware stack, spanning workload behavior and performance qualification as well as platform health and root-cause isolation. The role demands strong technical judgment, disciplined failure isolation, and the ability to trace unexpected behavior from application to hardware, working across firmware, kernel, compiler, library, and framework teams to drive issues to resolution.
KEY RESPONSIBILITIES:- Debug GPU failures including hangs, crashes, memory faults, firmware errors, and performance regressions across the full software and hardware stack.
- Reproduce failures and reduce them to minimal, actionable test cases.
- Triage system-level interactions across GPU, CPU, memory, networking, power, and platform topology.
- Validate AI, HPC, and communication workloads across GPU platforms, firmware, and software releases.
- Characterize performance and identify compute, memory, and communication bottlenecks.
- Analyze single- and multi-GPU behavior across workload configurations, topology, NUMA, power, and thermal conditions.
- Establish benchmarks, baselines, and regression-detection methods; correlate microbenchmark results with real workload behavior.
- Build automated diagnostics, stress tests, performance suites, and regression infrastructure.
- Support customer-representative validation and escalation reproduction.
REQUIRED QUALIFICATIONS:- Strong Linux and systems-engineering experience, with C/C++, Python, and shell automation.
- Experience with data center GPUs, accelerators, or comparable high-performance systems.
- Understanding of GPU architecture, memory hierarchy, parallel execution, communication, and synchronization.
- Experience debugging across software, firmware, kernel, and hardware boundaries.
- Ability to analyze logs, traces, hardware telemetry, and performance counters.
PREFERRED EXPERIENCE:- Strong knowledge of Linux kernel drivers, PCIe, firmware interaction, and memory management.
- Familiarity with GPU programming models and runtimes such as ROCm/HIP, RCCL, or CUDA equivalents.
- Experience with AI training or inference frameworks such as PyTorch, vLLM, or SGLang.
- Experience benchmarking AI inference, training, or HPC workloads, including roofline analysis and model-level profiling.
- Experience with profiling tools, statistical analysis, and regression detection.
- Familiarity with multi-GPU topology, PCIe/fabric interconnects, NUMA, GPU firmware, RAS, and rack-scale platforms.
- Experience with system bring-up, qualification, or production data center operations.
- Familiarity with CI systems, automated validation infrastructure, and fleet-scale testing.
ACADEMIC CREDENTIALS: - Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent
This role is not eligible for visa sponsorship.#LI-G11
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.
