AI Vision Processors For Edge Applications

Our solutions make cameras smarter by extracting valuable data from high-resolution video streams.

Job Description

Join us to build Classical CV and NNISP acceleration pipelines on CVflow, and improve system performance and efficiency.

We are looking for engineers passionate about embedded systems and computer vision — fresh graduates and experienced candidates are welcome.

___________________________________________________________________________

About the Team

We are the CV Accelerator team within the DSP department,focused on delivering high-performance edge-side computer vision processing on Ambarella’s proprietary CVflow™ architecture.

Our work targets real-world applications requiring low latency, low power, and real-time performance at the edge.

___________________________________________________________________________

What You’ll Do

• Port and optimize classical (non-neural-network) computer vision algorithms onto CVflow (e.g., image processing, filtering, optical flow, radar/LiDAR point cloud processing)

• Perform NNISP model porting for neural network–based ISP pipelines

• Develop efficient implementations on:

 NVP (Neural Vector Processor)

 GVP (General Vector Processor)

• Optimize compute performance and memory efficiency

• Ensure real-time performance on edge hardware platforms

• Leverage modern AI-assisted coding tools to improve productivity

• Collaborate with cross-functional teams (algorithm / system / hardware)

___________________________________________________________________________

Why Join Us

• Work on edge AI / computer vision acceleration hardware (CVflow)

• Gain experience in both classical CV and NNISP pipelines

• Tackle real-world performance-critical systems

• Grow into an expert in edge computer vision and hardware-aware optimization

• Be part of a team using modern, high-productivity development workflows

Preferred Qualifications

• Familiarity with embedded systems (ARM-based)

• Understanding of multithreading (Linux or RTOS)

• Experience in debugging and performance optimization

• Background in image processing or computer vision is a strong plus

• Familiarity with Python or deep learning frameworks (e.g., PyTorch) is a plus, especially for NNISP-related development

___________________________________________________________________________

Nice to Have

• Experience with edge AI or computer vision pipelines

• Exposure to hardware accelerators, SIMD, or vector processing

• Experience with AI-assisted coding tools (e.g., Cursor or similar)

___________________________________________________________________________

Experience Level

• Fresh graduates are welcome

• Candidates with relevant experience are preferred