Principal/Sr. GPU Software Performance Engineer

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WHAT YOU DO AT AMD CHANGES EVERYTHING

At AMD, our mission is to build great products that accelerate next-generation computing experiences-from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges-striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career.

THE ROLE:

We train large models across multiGPU clusters. Your charter is to make training materially faster and cheaper by leading kernellevel performance engineering-from math kernels and fused epilogues to clusterlevel throughput-partnering with researchers, framework teams, and infrastructure.

KEY RESPONSIBILITIES:

• Own kernel performance: Design, implement, and land highimpact HIP/C++ kernels (e.g., attention, layernorm, softmax, GEMM/epilogues, fused pointwise) that are wavesize portable and optimized for LDS, caches, and MFMA units.
• Lead profiling & tuning: Build repeatable workflows with timelines, hardware counters, and roofline analysis; remove memory bottlenecks; tune launch geometry/occupancy; validate speedups with A/B harnesses.
• Drive fusion & algorithmic improvements: Identify profitable fusions, tiling strategies, vectorized I/O, sharedmemory/scratchpad layouts, asynchronous pipelines, and warp/wavelevel collectives-while maintaining numerical stability.
• Influence frameworks & libraries: Upstream or extend performancecritical ops in PyTorch/JAX/XLA/Triton; evaluate and integrate vendor math libraries; guide compiler/codegen choices for target architectures
• Scale beyond one GPU: Optimize P2P and collective comms, overlap compute/comm, and improve data/pipeline/tensor parallelism throughput across nodes

• Benchmarking & SLOs: Define and own KPIs (throughput, timetotrain, $/step, energy/step); maintain dashboards, perf CI gates, and regression triage
• Technical leadership: Mentor senior engineers, set coding/perf standards, lead performance “war rooms,” and partner with silicon/vendor teams on microarchitectureaware optimizations
• Quality & reliability: Build reproducible perf harnesses, deterministic test modes, and documentation/playbooks so improvements persist releaseoverrelease

PREFERRED experience:

• Experience in systems/HPC/ML performance engineering, with handson GPU kernel work and shipped optimizations in production training or HPC

• Expert in modern C++ (C++17+) and at least one GPU programming model (CUDA, HIP, or SYCL/oneAPI) or a GPU kernel DSL (e.g., Triton); comfortable with templates, memory qualifiers, atomics, and warp/wavelevel collectives
• Deep understanding of GPU microarchitecture: SIMT execution, occupancy vs. register/scratchpad pressure, memory hierarchy (global/L2/shared or LDS), coalescing, bank conflicts, vectorization, and instructionlevel parallelism
• Proficiency with profiling & analysis: timelines and counters (e.g., Nsight Systems/Compute, rocprof/Omniperf, VTune/GPA or equivalents), ISA/disassembly inspection, and correlating metrics to code changes
• Proven track record reducing timetotrain or $perstep via kernel and collectivecomms optimizations on multiGPU clusters
• Strong Linux fundamentals (perf/eBPF, NUMA, PCIe/links), build systems (CMake/Bazel), Python, and containerized dev (Docker/Podman)

• Experience with distributed training (PyTorch DDP/FSDP/ZeRO/DeepSpeed or JAX) and GPU collectives
• Expertise in mixed precision (BF16/FP16/FP8), numerics, and stability/accuracy validation at kernel boundaries
• Background in compiler/IR (LLVM/MLIR) or codegen for GPU backends; ability to guide optimization passes with performance goals
• Handson with cluster orchestration (Slurm/Kubernetes), IB/RDMA tuning, and compute/communication overlap strategies

ACADEMIC CREDENTIALS:

• Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or equivalent

LOCATION:

San Jose, CA

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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.

AMD does not accept unsolicited resumes from headhunters, recruitment agencies or fee based recruitment services. AMD and its subsidiaries are equal opportunity employers. We consider candidates regardless of 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.

 

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