PE- CPU Software & Hardware Co-Design Engineer (ML Systems)
Qualcomm · Bengaluru · onsite · Posted 28d ago
Undisclosed8–13 yrsfulltimeSourced from
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About this role
Job 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.
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
- 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
- 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
- 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
- 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
- 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
- Strong background in:
- Computer Architecture / Systems Programming
- Machine Learning fundamentals
- Proficiency in:
- C/C++ (mandatory)
- Experience with:
- Performance profiling, benchmarking, and optimization
- 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
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Skills
Kernel Developmentcomputer architecturecachingKernelCompetitive AnalysisCachedesign engineerDesign SystemscharacterizationEmbedded SystemscloudDigital TransformationJavaArchitectureHardware DesignArtificial IntelligenceMachine Learning
About Qualcomm
Qualcomm
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