Qualcomm
Machine Learning / Computer Vision Engineer
$130K–$194K
San Diego, California · On-site · Full-time · Posted Sep 17, 2026
- Experience
- Bachelor’s: 2+ years of related work experience; master’s: 1+ year; PhD: no years specified. Preferred: 3+ years developing and implementing computer vision and video algorithms in system-level products; 3+ years in a large, matrixed organization.
- Education
- Bachelor’s or master’s degree in Computer or Electrical Engineering, Computer Science, or a related field; or PhD in those fields.
About the job
Researches, develops, trains, and optimizes computer vision and video algorithms and deep-learning models for deployment on resource-constrained edge devices and SoC platforms.
What you'll do
- Design and implement video and computer vision algorithms, including depth estimation, optical flow, super-resolution, denoising, 3D reconstruction, visual odometry, and SLAM.
- Develop and train edge-deployable deep-learning models, then optimize their performance under memory, compute, power, and bandwidth constraints.
- Collaborate across hardware, software, systems, and product teams to deliver and release algorithms that meet customer requirements.
What they ask for
- Bachelor’s degree in Computer or Electrical Engineering, Computer Science, or a related field and 2+ years of related work experience; or a master’s degree in those fields and 1+ year of related work experience; or a PhD in those fields.
Nice to have
- Strong computer vision and video-processing background; 3+ years developing and implementing such algorithms in system-level products.
- Experience with deep-learning architectures, Python, ML/CV frameworks, edge deployment, or AI coding assistants; publications, patents, or external technical contributions are a plus.
Pay
$129,500.00 - $194,300.00 (base salary; ISO currency code not stated)
Tools
Python, C/C++, PyTorch, TensorFlow, ONNX, OpenCV, ExecuTorch, Jetpack, QNN
AI focus
Develops and trains deep-learning models for edge computer vision, drawing on advances such as transformers, attention, VLA/VLM, and world models; optimizes deployment on resource-constrained platforms.
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