Mobile Computer Vision Architect: HW-Aware DL Engines

Qualcomm; San Diego, Northern (CA, KY); Hybrid; USD 123,000 - 213,000; Full time

Job summary

Qualcomm Technologies, Inc. is seeking engineers to advance mobile computer vision and AI systems architecture.

You will design architectures for hardware-aware deep learning engines and accelerators across CPUs, GPUs, DSPs, NPUs, and custom accelerators, targeting power-efficient, real-time performance on heterogeneous mobile platforms. The role requires strong knowledge of computer vision and neural networks, with experience in HW/SW co-design, energy-efficient optimization, and architecture

Qualifications

• Bachelor's degree with 2+ years of software/hardware/systems experience.
• Master's degree with 1+ year of software/hardware/systems experience.
• PhD in related field.

Responsibilities

• Study state-of-the-art computer vision and deep learning models and map to mobile AI accelerators.
• Drive architecture development for hardware-aware deep learning engines and dataflows.
• Analyze workloads to improve performance, power, memory bandwidth, and silicon area.
• Define HW/SW partitioning across CPUs, GPUs, DSPs, NPUs, and accelerators.
• Develop workload characterization methodologies and performance models.
• Collaborate with hardware teams to define next-gen AI engine features.
• Drive top-down architecture exploration from algorithms to hardware implementations.

Skills

C/C++

Python

Algorithm prototyping

Performance analysis

Education

Bachelor's degree in Computer or Electrical Engineering, Computer Science, or related field

Master's degree in Computer or Electrical Engineering, Computer Science, or related field

PhD in Computer or Electrical Engineering, Computer Science, or related field

Job description

Qualcomm Technologies, Inc. is seeking engineers to advance mobile computer vision and AI systems architecture.

You will design architectures for hardware-aware deep learning engines and accelerators across CPUs, GPUs, DSPs, NPUs, and custom accelerators, targeting power-efficient, real-time performance on heterogeneous mobile platforms. The role requires strong knowledge of computer vision and neural networks, with experience in HW/SW co-design, energy-efficient optimization, and architecture