Principal Software Quality Engineer – GPU & Machine Learning

THE ROLE: 

We are seeking a Principal Software Quality Engineer to serve as the senior technical leader for ROCm software validation across compute workloads and server-class systems. In this individual-contributor leadership role, you will define how AMD proves ROCm is ready to ship — from unit and component testing, through full-stack workload validation, to multi-node system-level qualification on AMD Instinct™ GPU platforms.

THE PERSON:

You will set the technical direction for validation strategy, build and evolve the test infrastructure that gates every ROCm release, and personally drive the hardest debugging, characterization, and qualification problems. Your work directly determines the quality bar experienced by hyperscalers, OEMs, sovereign-AI customers, and the open-source community running ROCm in production. 

KEY RESPONSIBILITIES:

• Own the end-to-end validation architecture for ROCm — unit, integration, framework, workload, performance, stress, stability, scale-out, and system-level test layers — across multiple GPU generations and server platforms. 

• Define release-qualification gates and exit criteria for ROCm software releases (functional coverage, performance regressions, stability hours, scale targets, RAS criteria) and drive the org to meet them. 

• Lead system-level testing for server nodes — multi-GPU topologies, PCIe/Infinity Fabric/xGMI, BMC/IPMI, thermal/power, firmware interactions, and multi-node fabric (Ethernet/InfiniBand/UALink) bring-up and validation. 

• Drive compute workload validation and characterization — LLM training and inference (PyTorch, vLLM, Triton, JAX), recommender systems, scientific HPC kernels, MLPerf-class benchmarks — establishing reproducible methodology, baselines, and regression tracking. 

• Architect the test infrastructure — distributed test runners, GitHub Actions / Jenkins / internal CI fleets, hardware lab orchestration, result data lakes, flaky-test detection, bisection automation, and self-service developer pre-submit pipelines. 

• Champion modern, agile quality engineering — shift-left testing, test pyramids, contract testing between layers, hermetic test environments, deterministic reproducers, and continuous validation in trunk. 

• Set the bar for GitHub-based quality workflows — PR gating policy, required checks, code-coverage standards, bug-bash and triage cadences, and disciplined issue management across ROCm/* repositories and partner upstream projects. 

• Lead complex escalation debug — partner with development, hardware, firmware, and customer-facing teams to root-cause the hardest multi-day, multi-node, multi-component failures and convert findings into durable test coverage. 

• Influence the roadmap — work with product management, silicon, platform, and software architecture to ensure validation readiness for next-generation Instinct GPUs and server platforms before tape-in milestones and silicon arrival. 

• Mentor and elevate Senior and Staff validation engineers, SDETs, and SQA leads; raise the technical bar through design review, code review, and written guidance. 

• Represent ROCm validation externally — strategic customer engagements, OEM qualification programs, and open-source community quality initiatives. 

PREFERRED EXPERIENCE:

• Strong software engineering experience with a strong validation, SDET, or quality-engineering focus, including 5+ years in a senior IC role (Staff/Principal/PMTS or equivalent) leading validation of complex systems software. 

• Expert-level Python for test automation and infrastructure; strong C++ for debugging and extending production code paths under test. 

• Deep, demonstrable validation experience in at least two of the following domains: 

• GPU compute software stacks (ROCm, CUDA, oneAPI, SYCL) 

• Deep-learning frameworks and inference engines (PyTorch, TensorFlow, JAX, Triton, vLLM) 

• HPC / parallel runtimes and communication libraries (MPI, RCCL/NCCL, UCX, Libfabric) 

• Linux kernel, GPU drivers, or accelerator firmware 

• Distributed systems and large-scale cluster software 

• System-level validation for server-class compute nodes — multi-GPU, multi-node, fabric-attached environments — including stress/stability, soak, fault-injection, and RAS testing. 

• Proven, hands-on experience working efficiently in an agentic AI engineering environment — daily, production use of LLM-based coding agents (e.g., Cursor, Claude Code, Copilot Workspace, Codex-class agents) and orchestration frameworks for real engineering work, with demonstrable productivity, quality, or coverage gains attributable to those workflows. Comfort designing prompts, tool/MCP integrations, evaluation harnesses, and guardrails for autonomous and semi-autonomous agents. 

• Hands-on experience defining and shipping release qualification programs for software consumed by hyperscalers, OEMs, or other Tier-1 customers. 

• Mastery of GitHub at scale for quality engineering — PR gating, GitHub Actions, self-hosted runners, required status checks, release tagging, and open-source contribution and triage norms. 

• Strong command of modern, agile software development practices — trunk-based development, CI/CD, shift-left testing, observability, feature flags, and incremental delivery — applied specifically to validation organizations. 

• Excellent written and verbal communication — able to author crisp test plans, qualification reports, RFCs, and post-mortems, and to influence development teams without authority. 

• Direct contributions to validation, CI, or test infrastructure for ROCm, PyTorch, LLVM, Triton, vLLM, or comparable upstream open-source projects. 

• Demonstrated leadership in agentic-AI adoption — built or rolled out agent-based workflows across an engineering team (e.g., autonomous test generation, AI-driven log/triage pipelines, multi-agent debug systems, MCP server design, retrieval-augmented engineering knowledge bases) with measurable outcomes. 

• Experience operating or validating large GPU clusters (256+ GPUs) — fabric bring-up, cluster health monitoring, and fleet-level diagnostics. 

• Familiarity with Training/Inference/HPC industry-standard benchmark methodologies and submissions. 

• Background in performance validation: roofline analysis, profiler tooling (rocprof, Omniperf, Nsight-class), regression detection 

• Experience with fault injection, RAS, telemetry, and long-haul stability programs for accelerator platforms. 

• Familiarity with hardware lab automation: BMC/IPMI/Redfish, PDU control, serial-console capture, automated re-imaging, and topology-aware test scheduling. 

• Prior experience standing up validation for pre-silicon / emulation / first-silicon bring-up of accelerators. 

ACADEMIC CREDENTIALS: 

LOCATION: San Jose, California

#LI-DR1

#LI-HYBRID 

THE ROLE: 

We are seeking a Principal Software Quality Engineer to serve as the senior technical leader for ROCm software validation across compute workloads and server-class systems. In this individual-contributor leadership role, you will define how AMD proves ROCm is ready to ship — from unit and component testing, through full-stack workload validation, to multi-node system-level qualification on AMD Instinct™ GPU platforms.

THE PERSON:

You will set the technical direction for validation strategy, build and evolve the test infrastructure that gates every ROCm release, and personally drive the hardest debugging, characterization, and qualification problems. Your work directly determines the quality bar experienced by hyperscalers, OEMs, sovereign-AI customers, and the open-source community running ROCm in production. 

KEY RESPONSIBILITIES:

• Own the end-to-end validation architecture for ROCm — unit, integration, framework, workload, performance, stress, stability, scale-out, and system-level test layers — across multiple GPU generations and server platforms. 

• Define release-qualification gates and exit criteria for ROCm software releases (functional coverage, performance regressions, stability hours, scale targets, RAS criteria) and drive the org to meet them. 

• Lead system-level testing for server nodes — multi-GPU topologies, PCIe/Infinity Fabric/xGMI, BMC/IPMI, thermal/power, firmware interactions, and multi-node fabric (Ethernet/InfiniBand/UALink) bring-up and validation. 

• Drive compute workload validation and characterization — LLM training and inference (PyTorch, vLLM, Triton, JAX), recommender systems, scientific HPC kernels, MLPerf-class benchmarks — establishing reproducible methodology, baselines, and regression tracking. 

• Architect the test infrastructure — distributed test runners, GitHub Actions / Jenkins / internal CI fleets, hardware lab orchestration, result data lakes, flaky-test detection, bisection automation, and self-service developer pre-submit pipelines. 

• Champion modern, agile quality engineering — shift-left testing, test pyramids, contract testing between layers, hermetic test environments, deterministic reproducers, and continuous validation in trunk. 

• Set the bar for GitHub-based quality workflows — PR gating policy, required checks, code-coverage standards, bug-bash and triage cadences, and disciplined issue management across ROCm/* repositories and partner upstream projects. 

• Lead complex escalation debug — partner with development, hardware, firmware, and customer-facing teams to root-cause the hardest multi-day, multi-node, multi-component failures and convert findings into durable test coverage. 

• Influence the roadmap — work with product management, silicon, platform, and software architecture to ensure validation readiness for next-generation Instinct GPUs and server platforms before tape-in milestones and silicon arrival. 

• Mentor and elevate Senior and Staff validation engineers, SDETs, and SQA leads; raise the technical bar through design review, code review, and written guidance. 

• Represent ROCm validation externally — strategic customer engagements, OEM qualification programs, and open-source community quality initiatives. 

PREFERRED EXPERIENCE:

• Strong software engineering experience with a strong validation, SDET, or quality-engineering focus, including 5+ years in a senior IC role (Staff/Principal/PMTS or equivalent) leading validation of complex systems software. 

• Expert-level Python for test automation and infrastructure; strong C++ for debugging and extending production code paths under test. 

• Deep, demonstrable validation experience in at least two of the following domains: 

• GPU compute software stacks (ROCm, CUDA, oneAPI, SYCL) 

• Deep-learning frameworks and inference engines (PyTorch, TensorFlow, JAX, Triton, vLLM) 

• HPC / parallel runtimes and communication libraries (MPI, RCCL/NCCL, UCX, Libfabric) 

• Linux kernel, GPU drivers, or accelerator firmware 

• Distributed systems and large-scale cluster software 

• System-level validation for server-class compute nodes — multi-GPU, multi-node, fabric-attached environments — including stress/stability, soak, fault-injection, and RAS testing. 

• Proven, hands-on experience working efficiently in an agentic AI engineering environment — daily, production use of LLM-based coding agents (e.g., Cursor, Claude Code, Copilot Workspace, Codex-class agents) and orchestration frameworks for real engineering work, with demonstrable productivity, quality, or coverage gains attributable to those workflows. Comfort designing prompts, tool/MCP integrations, evaluation harnesses, and guardrails for autonomous and semi-autonomous agents. 

• Hands-on experience defining and shipping release qualification programs for software consumed by hyperscalers, OEMs, or other Tier-1 customers. 

• Mastery of GitHub at scale for quality engineering — PR gating, GitHub Actions, self-hosted runners, required status checks, release tagging, and open-source contribution and triage norms. 

• Strong command of modern, agile software development practices — trunk-based development, CI/CD, shift-left testing, observability, feature flags, and incremental delivery — applied specifically to validation organizations. 

• Excellent written and verbal communication — able to author crisp test plans, qualification reports, RFCs, and post-mortems, and to influence development teams without authority. 

• Direct contributions to validation, CI, or test infrastructure for ROCm, PyTorch, LLVM, Triton, vLLM, or comparable upstream open-source projects. 

• Demonstrated leadership in agentic-AI adoption — built or rolled out agent-based workflows across an engineering team (e.g., autonomous test generation, AI-driven log/triage pipelines, multi-agent debug systems, MCP server design, retrieval-augmented engineering knowledge bases) with measurable outcomes. 

• Experience operating or validating large GPU clusters (256+ GPUs) — fabric bring-up, cluster health monitoring, and fleet-level diagnostics. 

• Familiarity with Training/Inference/HPC industry-standard benchmark methodologies and submissions. 

• Background in performance validation: roofline analysis, profiler tooling (rocprof, Omniperf, Nsight-class), regression detection 

• Experience with fault injection, RAS, telemetry, and long-haul stability programs for accelerator platforms. 

• Familiarity with hardware lab automation: BMC/IPMI/Redfish, PDU control, serial-console capture, automated re-imaging, and topology-aware test scheduling. 

• Prior experience standing up validation for pre-silicon / emulation / first-silicon bring-up of accelerators. 

ACADEMIC CREDENTIALS: 

LOCATION: San Jose, California

#LI-DR1

#LI-HYBRID