PE- CPU Software & Hardware Co-Design Engineer (ML Systems)

Identify and prioritize critical ML use cases and models for CPU-centric execution (LLMs, vision, speech, recommender systems, etc.)

Analyze workload characteristics including: Compute intensity Memory bandwidth and cache behavior Parallelism and dataflow patterns

Compute intensity

Memory bandwidth and cache behavior

Parallelism and dataflow patterns

Generate detailed execution traces for ML workloads using QEMU or equivalent simulators

Develop tooling to: Capture instruction-level execution behavior Extract performance counters and bottlenecks

Capture instruction-level execution behavior

Extract performance counters and bottlenecks

Enable accurate modeling of workload behavior for architectural exploration

Identify system bottlenecks across: CPU pipelines Memory hierarchy Instruction utilization

CPU pipelines

Memory hierarchy

Instruction utilization

Optimize critical hotspots through: Kernel-level tuning Algorithmic improvements Data layout and memory optimizations

Kernel-level tuning

Algorithmic improvements

Data layout and memory optimizations

Drive measurable improvements in workload performance

Collaborate with CPU architecture and design teams to: Provide data-driven insights from real workloads Identify inefficiencies and propose architectural enhancements

Provide data-driven insights from real workloads

Identify inefficiencies and propose architectural enhancements

Influence next-generation CPU features in: Compute units Vector/SIMD extensions (e.g., QMX) Memory subsystems

Compute units

Vector/SIMD extensions (e.g., QMX)

Memory subsystems

Design and implement highly optimized ML kernels and libraries for QMX architecture

Develop kernels for: GEMM, convolution, attention, activation functions, etc.

GEMM, convolution, attention, activation functions, etc.

Enable integration with: Open-source ML frameworks (e.g., PyTorch, ONNX, XNNPACK, MLAS)

Open-source ML frameworks (e.g., PyTorch, ONNX, XNNPACK, MLAS)

Apply advanced optimizations: SIMD/vectorization Cache-aware execution Parallel execution strategies

SIMD/vectorization

Cache-aware execution

Parallel execution strategies

Optimize CPU-centric ML benchmarks such as: Geekbench AI Internal benchmarking suites

Geekbench AI

Internal benchmarking suites

Establish performance baselines and track improvements across hardware generations

Perform competitive analysis and performance positioning

Work on next-generation CPU architectures (QMX)

Directly influence hardware design through real workload insights

Solve end-to-end ML performance challenges (model → kernel → silicon)

Collaborate with top architecture, systems, and AI teams

High-impact role with visibility across product and research roadmaps

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