Data Architect

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, toolchains, 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 Requirements 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, TensorFlow, 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 toolchains, including Qualcomm Hexagon/QNN, MediaTek APU/NeuroPilot, 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 Core Competencies 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. Highest-signal resume keywords AI/ML Frameworks (PyTorch, TensorFlow, JAX, ONNX) Edge Machine Learning Deployment GPU/NPU/DSP Architecture Familiarity Technical Strategy Definition Real-Time Audio/Video Inference Pipelines Hard Skills Machine Learning Embedded Software Engineering Production-Quality Code Model Optimization Techniques (Quantization, Pruning, Distillation) Audio/Video Signal Processing Soft Skills Collaboration Influencing Cross-Functional Teams Technical Communication Industry Keywords Data Governance Standards Generative AI Models Dolby Technologies (Atmos, Vision, AC-4) Media Codecs Open-Source ML Contributions Tools & Technologies Version Control Systems Build Systems Software Delivery Pipelines Qualcomm Hexagon/QNN MediaTek APU/NeuroPilot #J-18808-Ljbffr