PE- CPU Software & Hardware Co-Design Engineer (ML Systems)

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.

Location: Bangalore

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 softwarehardware 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 stackfrom ML models to low-level kernels to architectural feedbackenabling efficient execution of ML workloads on CPU platforms.

• 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
• SoftwareHardware 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

• 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

• 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