Lead Engineer,Senior-ARM,CPU

## 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 3+ years of Software Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience. • 2+ years of academic or 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 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...