Engineer/Senior Engineer- ML Libraries/ARM

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 2+ years of Software Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field. • 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 intensityMemory bandwidth and cache behaviorParallelism 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 behaviorExtract performance counters and bottlenecks • Enable accurate modeling of workload behavior for architectural exploration 3. Bottleneck Analysis & Performance Optimization • Identify 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 4. Software–Hardware Co-Design • Collaborate 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 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/vectorizationCache-aware executionParallel 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 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 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. 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