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 & 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
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
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
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
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
Benchmarking & Performance Engineering
Optimize CPU-centric ML benchmarks such as:
Geekbench AI
Internal 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 simulators
ML 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 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
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).
Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
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If you would like more information about this role, please contact Qualcomm Careers.

Work arrangement
No