Drive architecture development for hardware-aware deep learning engines, including support for emerging neural network operators, dataflows, tensor processing pipelines, quantization techniques, and memory hierarchies.
Analyze neural network workloads and identify architectural enhancements required to improve performance, power efficiency, memory bandwidth utilization, and silicon area.
Define HW/SW partitioning strategies across CPUs, GPUs, DSPs, NPUs, and dedicated accelerators.
Develop workload characterization methodologies and performance models for computer vision and AI applications.
Collaborate with hardware architects and designers to define next-generation AI engine features and capabilities based on evolving computer vision workloads.
Drive top-down architecture exploration from algorithm requirements through hardware implementation, including performance, power, thermal, and area projections.
Minimum Qualifications
- Bachelor's degree in Computer or Electrical Engineering, Computer Science, or related field and 2+ years of Software Engineering, Hardware Engineering, Systems Engineering, or related work experience.
- OR
- Master's degree in Computer or Electrical Engineering, Computer Science, or related field and 1+ year of Software Engineering, Hardware Engineering, Systems Engineering, or related work experience.
- OR
- PhD in Computer or Electrical Engineering, Computer Science, or related field.
Preferred Qualifications
Multiple years of experience developing mobile computer vision and AI systems.
Deep knowledge of modern neural network architectures, including CNNs, Transformers, Vision Transformers (ViTs), multi-modal perception systems, and hardware-aware model optimization techniques.
Experience architecting and optimizing deep learning engines (DLEs) or AI accelerators for computer vision workloads.
Additional Requirements
Strong understanding of mobile computer vision applications, including: Object Detection and Tracking, Segmentation, Scene Understanding, Image Enhancement and Computational Photography, Motion Estimation and Neural Network-Based Vision Systems
Expertise in system architecture and HW/SW partitioning for computer vision and AI workloads.
Strong understanding of deep learning accelerator architectures, including Tensor processing, optimization, compute and neural network bottlenecks
Experience defining real-time hardware architectures for computer vision and deep learning workloads.
Experience evaluating throughput, latency, power, memory bandwidth, and silicon area trade-offs.
Strong programming skills in C/C++ and Python with hands-on experience in algorithm prototyping and performance analysis.
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Pay range and Other Compensation & Benefits
$129,500.00 - $194,300.00
The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer – and you can review more details about our US benefits at this link.
If you would like more information about this role, please contact Qualcomm Careers.
