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 ========== 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,…