AI Architect

Join the leader in entertainment innovation and help us design the future. At Dolby, science meets art, and high tech means more than computer code. As a member of the Dolby team, you'll see and hear the results of your work everywhere, from movie theaters to smartphones. We continue to revolutionize how people create, deliver, and enjoy entertainment worldwide. To do that, we need the absolute best talent.

We're big enough to give you all the resources you need, and small enough so you can make a real difference and earn recognition for your work. We offer a collegial culture, challenging projects, and excellent compensation and benefits, not to mention a Flex Work approach that is truly flexible to support where, when, and how you do your best work.

The Dolby Cloud Solutions organization builds technologies and innovations that easily integrate into service providers' infrastructure to make content experiences more effective, meaningful, and engaging for consumers.

Role Overview

We are seeking a Senior Staff AI / Machine Learning Architect to serve as a key technical bridge between research teams and product engineering organizations. In this role, you will help translate advanced machine learning research into efficient, scalable, and production ready solutions across Dolby's product portfolio.

You will play a critical role in defining technical strategy for developing, training, and deploying AI/ML models-particularly in edge ML and NPU enabled platforms-while collaborating closely with researchers, software engineers, and external partners such as silicon vendors. This highly cross functional role combines hands on technical expertise with system level thinking and technical leadership, influencing direction across projects and teams through execution and clear technical communication.

Key Responsibilities

Technical Strategy and Leadership

• Define and guide AI/ML technology strategy across Dolby's core technology areas (audio processing, video processing, personalization, and related domains), spanning cloud, edge, and embedded environments, with a focus on edge ML, GPUs, and NPUs
• Anticipate evolving business and technical needs and contribute to a forward looking technical vision
• Establish best practices, guardrails, and technical guidelines for building, training, optimizing, and deploying ML models across the organization
• Stay current with developments in AI/ML, including emerging architectures and edge inference techniques, and translate industry trends into practical, production oriented recommendations for accelerated hardware

Bridge Research and Engineering

• Serve as a primary technical interface between ML research teams and engineering teams
• Define architectural approaches for integrating traditional audio/video processing (DSPs, hardware accelerators) with ML models
• Partner with platform managers and engineering teams to integrate ML models into shipped products, and collaborate with researchers to align on requirements and constraints
• Work with Data Engineering teams to help establish data governance guidelines and standards for data sourcing, cleaning, and pipeline management
• Collaborate with QA teams to develop testing methodologies appropriate for AI/ML systems

Engage with Silicon Vendors

• Develop a working understanding of GPU and NPU architectures, tool chains, operator support, and performance characteristics
• Identify gaps between model requirements and hardware capabilities, and help drive solutions in collaboration with internal teams and external partners
• Collaborate with and influence silicon vendors and platform partners on roadmap alignment, tooling, and hardware capabilities relevant to Dolby use cases

Hands On Technical Work

• Conduct technical investigations and experiments, including profiling models, benchmarking inference, and evaluating accuracy latency trade offs
• Apply and advise on model optimization techniques such as retraining, pruning, quantization, distillation, and hardware aware optimization
• Guide model porting across frameworks and runtimes (e.g., PyTorch → ONNX → vendor specific runtimes)
• Build prototypes and proof of concepts to reduce technical risk prior to full engineering investment

Qualifications

Required

• 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 (often acquired over many years of professional practice)
• Strong software engineering skills, including experience writing production quality code and working with version control, testing, build systems, and software delivery pipelines
• Experience with at least one major AI/ML framework (e.g., PyTorch, Tensor Flow, JAX, ONNX) and the ability to learn additional frameworks as needed
• Hands on experience deploying optimized ML models (e.g., quantization, pruning, distillation, operator fusion)
• Experience with edge or on device ML, including awareness of constraints such as…