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:

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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 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: …