CPU Software & Hardware Co-Design Engineer; ML Systems Remote, Software Engineering Posted

Position: CPU Software & Hardware Co-Design Engineer (ML Systems Remote, United States of America Software Engineering Posted a
## CPU Software & Hardware Co-Design Engineer (ML Systems)
Florida - Remote, United States of America##
** Company:
** Qualcomm Technologies, Inc.##
** Job Area:
** Engineering Group, Engineering Group  Software Engineering
** General

Summary:

** As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Software Engineer, you will design, develop, create, modify, and validate embedded and cloud edge software, applications, and/or specialized utility programs that launch cutting-edge, world class products that meet and exceed customer needs.

Qualcomm Software Engineers collaborate with systems, hardware, architecture, test engineers, and other teams to design system-level software solutions and obtain information on performance requirements and interfaces.
** Role Overview
** We are building a high-impact team at the intersection of
** CPU architecture, machine learning workloads, and system-level performance optimization**. This role focuses on
** CPU software-hardware co-design for next-generation QMX architectures**, including workload characterization, simulation, kernel optimization, and driving architectural insights for future CPU designs. The ideal candidate will work across the full stack - from ML models to low-level kernels to architectural feedback - enabling
** efficient execution of ML workloads on CPU platforms**.
** Key Responsibilities
**** 1. ML Workload Identification & Characterization
*** 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
** 2. Simulation & Trace Generation
*** 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
* Enable accurate modeling of workload behavior for architectural exploration
** 3. Bottleneck Analysis & Performance Optimization
*** Identify system bottlenecks across:

CPU pipelines

Memory hierarchy

Instruction utilization
* Optimize critical hotspots through:

Kernel-level tuning

Algorithmic improvements

Data layout and memory optimizations
* Drive measurable improvements in workload performance
** 4. Software-Hardware Co-Design
*** Collaborate with CPU architecture and design teams to:

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
** 5. ML Kernel & Library Development (QMX Focus)
*** Design and implement
** highly optimized ML kernels and libraries
** for QMX architecture
* Develop kernels for:

GEMM, convolution, attention, activation functions, etc.
* Enable integration with:

Open-source ML frameworks (e.g., PyTorch, ONNX, XNNPACK, MLAS)
* Apply advanced optimizations:

SIMD/vectorization

Cache-aware execution

Parallel execution strategies
** 6. Benchmarking & Performance Engineering
*** Optimize CPU-centric ML benchmarks such as:

Geekbench AIInternal benchmarking suites
* Establish performance baselines and track improvements across hardware generations
* Perform competitive analysis and performance positioning
** Required Qualifications
*** Strong background in:

Computer Architecture / Systems Programming Machine Learning fundamentals
* Proficiency in:

C/C++ (mandatory)
* Experience with:

Performance profiling, benchmarking, and optimization
** Preferred Qualifications
*** Experience with:

QEMU or equivalent simulatorsML kernel development (GEMM, convolution, attention)
* Knowledge of:

CPU architecture (pipelines, caching, SIMD/vector extensions such as NEON, SVE, QMX)
* Familiarity with:

ML frameworks and inference stacks
* Experience with low-level optimization:

Intrinsics, assembly, memory and cache tuning
*
* Minimum Qualifications:

**- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 8+ years of Software Engineering or related work experience.

ORMaster's degree in Engineering, Information Systems, Computer Science, or related field and 7+ years of Software Engineering or related work experience.

ORPhD in Engineering, Information Systems, Computer Science, or related field and 6+…