This role pays less than
72%
of similar roles. Most pay
$161,437–$235,750
— the shaded band above.
At the midpoint, this role pays about
$168k
versus about
$199k
for comparable roles.
Based on 240 similar postings.
Employer
About Qualcomm
Qualcomm is a leading American semiconductor and telecommunications company based in San Diego, CA.
Qualcomm currently has
623 open roles
on FindRole.
Listed pay typically runs
$148,300–$222,500
across 603 roles with salary data.
The Computer Vision System Engineer joins the Mobile Computer Vision and AI Systems Architecture Team to develop next-generation mobile computer vision and deep learning solutions for imaging, perception, scene understanding, segmentation, tracking, and computational photography. This role involves defining architecture for deep learning engines and accelerators by analyzing neural network workloads to improve performance, power efficiency, and memory bandwidth. The engineer will drive HW/SW partitioning across CPUs, GPUs, DSPs, NPUs, and dedicated accelerators while developing workload characterization methodologies and performance models. Key technical requirements include expertise in CNNs, Transformers, Vision Transformers, and hardware-aware model optimization. Candidates must possess strong programming skills in C/C++ and Python to perform algorithm prototyping and performance analysis. The role focuses on solving the challenge of translating complex neural network algorithms into power-efficient, real-time implementations on heterogeneous mobile platforms for advanced AI-powered experiences.
Map state-of-the-art computer vision and deep learning models onto mobile AI accelerators and heterogeneous platforms.
Drive architecture development for deep learning engines including dataflows, tensor processing pipelines, and quantization techniques.
Analyze neural network workloads to identify architectural improvements for performance, power efficiency, and memory bandwidth.
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.
Conduct top-down architecture exploration from algorithm requirements through hardware implementation projections.
Collaborate with hardware designers to define next-generation AI engine features based on evolving workloads.
What we're looking for
Bachelor's degree in Computer or Electrical Engineering, Computer Science, or related field and 2+ years of engineering experience.
Master's degree in Computer or Electrical Engineering, Computer Science, or related field and 1+ year of engineering experience.
PhD in Computer or Electrical Engineering, Computer Science, or related field.
Expertise in system architecture and HW/SW partitioning for computer vision and AI workloads.
Strong understanding of deep learning accelerator architectures, including Tensor processing and neural network bottlenecks.
Experience defining real-time hardware architectures and evaluating throughput, latency, power, memory bandwidth, and silicon area trade-offs.
Strong programming skills in C/C++ and Python with experience in algorithm prototyping and performance analysis.
Multiple years of experience developing mobile computer vision and AI systems (preferred); deep knowledge of modern neural network architectures (preferred).
Computer Vision
Deep Learning
Python
C++
PyTorch
TensorFlow
Keras
Caffe
Linux
Android
QNX
Quantization
Pruning
Distillation
CPU
GPU
DSP
NPU
Containerization