Qualcomm India Private Limited
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:
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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:
- Experience with:
- Performance profiling, benchmarking, and optimization
Preferred Qualifications
- 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
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. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail [email protected] or call Qualcomm's toll-free number found here . Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).
Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
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If you would like more information about this role, please contact Qualcomm Careers .
