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

Company

Qualcomm India Private Limited

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.

Minimum Qualifications

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

OR

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

OR

PhD in Engineering, Information Systems, Computer Science, or related field and 6+ years of Software Engineering or related work experience.

4+ years of work experience with Programming Language such as C, C++, Java, Python, etc.

Detailed JD

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Job Description: CPU Software & Hardware Co-Design Engineer (ML Systems)

Location: Bangalore (or relevant)

Levels: Engineer / Senior Engineer / Staff / Principal Engineer

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

ML Workload Identification & CharacterizationIdentify and prioritize critical ML use cases and models for CPU-centric execution (LLMs, vision, speech, recommender systems, etc.)Analyze workload characteristics including:Compute intensityMemory bandwidth and cache behaviorParallelism and dataflow patterns

Simulation & Trace GenerationGenerate detailed execution traces for ML workloads using QEMU or equivalent simulatorsDevelop tooling to:Capture instruction-level execution behaviorExtract performance counters and bottlenecks

Enable accurate modeling of workload behavior for architectural exploration Bottleneck Analysis & Performance OptimizationIdentify system bottlenecks across:CPU pipelinesMemory hierarchyInstruction utilization

Optimize critical hotspots through:Kernel-level tuningAlgorithmic improvementsData layout and memory optimizations

Drive measurable improvements in workload performance Software–Hardware Co-DesignCollaborate with CPU architecture and design teams to:Provide data-driven insights from real workloadsIdentify inefficiencies and propose architectural enhancements

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

ML Kernel & Library Development (QMX Focus)Design and implement highly optimized ML kernels and libraries for QMX architectureDevelop 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/vectorizationCache-aware executionParallel execution strategies

Benchmarking & Performance EngineeringOptimize CPU-centric ML benchmarks such as:Geekbench AIInternal benchmarking suites

Establish performance baselines and track improvements across hardware generationsPerform competitive analysis and performance positioning

Required Qualifications

Strong background in:Computer Architecture / Systems ProgrammingMachine 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

Why Join This Team

Work on next-generation CPU architectures (QMX)Directly influence hardware design through real workload insightsSolve end-to-end ML performance challenges (model → kernel → silicon)Collaborate with top architecture, systems, and AI teamsHigh-impact role with visibility across product and research roadmaps

Applicants: Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).

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