Senior Edge AI Architect for Media & Entertainment

Dolby; Atlanta (GA); Hybrid; USD 152,000 - 209,000; Full time

Benefits offered by this job

Flex Work

Job summary

Dolby is seeking a Senior Staff AI / Machine Learning Architect to translate research into production solutions across Dolby’s product portfolio. You will define technical strategy for training and deploying AI/ML models on edge platforms, collaborating with researchers, engineers, and silicon vendors.

You will lead cross-functional efforts, prototype solutions, and influence direction across projects while handling hands-on optimization and hardware-aware performance tuning.

Qualifications

• Bachelor’s or Master’s degree in Electrical Engineering, Computer Science, or a related field, or equivalent practical experience.
• Significant hands on experience in AI, machine learning, and embedded software engineering.
• Strong software engineering skills with production quality code and CI/CD practices.

Responsibilities

• Define and guide AI/ML technology strategy across Dolby’s core technology areas, including edge ML, GPUs, and NPUs.
• Serve as a primary technical interface between ML research and engineering teams.
• Collaborate with silicon vendors on roadmap alignment and hardware capabilities.
• Conduct technical investigations, profiling, benchmarking, and model optimization for edge deployment.

Skills

AI/ML architecture

Edge ML

Embedded software engineering

Production quality code

Version control

Testing & build systems

PyTorch/TensorFlow/JAX/ONNX

Edge inference optimizations

DSP/Hardware acceleration

Audio/Video processing

Education

Bachelor’s or Master’s in Electrical Engineering, Computer Science, or related field

Tools

Qualcomm Hexagon/QNN

MediaTek APU/NeuroPilot

ARM Ethos

Apple Neural Engine

Job description

Dolby is seeking a Senior Staff AI / Machine Learning Architect to translate research into production solutions across Dolby’s product portfolio. You will define technical strategy for training and deploying AI/ML models on edge platforms, collaborating with researchers, engineers, and silicon vendors.

You will lead cross-functional efforts, prototype solutions, and influence direction across projects while handling hands-on optimization and hardware-aware performance tuning.