AI Architect – Media

• Define and guide AI/ML technology strategy across Dolby’s core technology areas, including audio processing, video processing, personalization, and related domains
• Develop strategy spanning cloud, edge, and embedded environments, with emphasis on edge ML, GPUs, and NPUs
• Anticipate business and technical needs and contribute to long-term technical vision
• Establish best practices, guardrails, and guidelines for building, training, optimizing, and deploying ML models
• Translate AI/ML industry trends and emerging architectures into production-oriented recommendations for accelerated hardware
• Serve as the primary technical interface between ML research and engineering teams
• Define architectures integrating traditional audio/video processing, DSPs, hardware accelerators, and ML models
• Partner with platform managers and engineering teams to integrate ML models into shipped products
• Collaborate with researchers to align requirements and constraints
• Help establish data governance standards for sourcing, cleaning, and pipeline management with Data Engineering
• Collaborate with QA teams on AI/ML testing methodologies
• Evaluate GPU and NPU architectures, tool chains, operator support, and performance
• Identify gaps between model requirements and hardware capabilities and drive solutions with internal teams and external partners
• Influence silicon vendors and platform partners on roadmaps, tooling, and hardware capabilities
• Conduct technical investigations and experiments, including model profiling, inference benchmarking, and accuracy/latency trade-off evaluation
• Apply and advise on retraining, pruning, quantization, distillation, and hardware-aware optimization
• Guide model porting across frameworks and runtimes such as PyTorch, ONNX, and vendor-specific runtimes
• Build prototypes and proofs of concept to reduce technical risk before engineering investment
• Translate advanced ML research into efficient, scalable, production-ready solutions across Dolby’s product portfolio

• 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, including production-quality code, version control, testing, build systems, and software delivery pipelines
• Experience with at least one major AI/ML framework, such as PyTorch, Tensor Flow, JAX, or ONNX
• Hands-on experience deploying optimized ML models using quantization, pruning, distillation, or operator fusion
• Experience with edge or on-device ML and constraints involving latency, power, memory, and thermal limits
• Familiarity with CPU, GPU, NPU, and DSP architectures and associated tool chains, including Qualcomm Hexagon/QNN, Media Tek APU/Neuro Pilot, ARM Ethos, or Apple Neural Engine
• Experience in audio, video, signal processing, media codecs, or closely related technical domains
• Ability to work across abstraction layers from model architecture to operator-level hardware performance
• Experience defining technical strategy and influencing cross-functional teams
• Experience shipping ML models to production on resource-constrained devices
• Experience with real-time audio/video inference pipelines
• Familiarity with Dolby technologies such as Atmos, Vision, or AC-4, or comparable media standards
• Experience with generative AI models in audio or video
• Contributions to open-source ML tools or peer-reviewed research

Demonstrates expertise in AI/ML technology strategy, focusing on edge ML, GPUs, and NPUs, while integrating audio and video processing with machine learning models. Proven ability to influence cross-functional teams and establish best practices for deploying optimized ML solutions in production environments.

• AI/ML Frameworks (PyTorch, Tensor Flow, JAX, ONNX)
• Edge Machine Learning Deployment
• GPU/NPU/DSP Architecture Familiarity
• Technical Strategy Definition
• Real-Time Audio/Video Inference Pipelines

• Machine Learning
• Embedded Software Engineering
• Production-Quality Code
• Model Optimization Techniques (Quantization, Pruning, Distillation)
• Audio/Video Signal Processing

• Collaboration
• Influencing Cross-Functional Teams
• Technical Communication

• Data Governance Standards
• Generative AI Models
• Dolby Technologies (Atmos, Vision, AC-4)
• Media Codecs
• Open-Source ML Contributions

• Version Control Systems
• Build Systems
• Software Delivery Pipelines
• Qualcomm Hexagon/QNN
• Media Tek APU/Neuro Pilot