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
C/C++ (mandatory)
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 disability-accomodations@qualcomm.com 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.
To all Staffing and Recruiting Agencies: Our Careers Site is only for individuals seeking a job at Qualcomm. Staffing and recruiting agencies and individuals being represented by an agency are not authorized to use this site or to submit profiles, applications or resumes, and any such submissions will be considered unsolicited. Qualcomm does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Qualcomm employees or any other company location. Qualcomm is not responsible for any fees related to unsolicited resumes/applications.
If you would like more information about this role, please contact Qualcomm Careers.
