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\u2019ll 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\u2019re 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.\n\nDolby\u2019s consumer entertainment and cinema businesses are bringing Dolby\u2019s breakthrough technologies, powering the world\u2019s top movies, TV shows, music, games, and live sports to more places around the world across a wider range of consumer experiences and devices.\n\nRole Overview\n\nWe 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\u2019s product portfolio.\n\nYou will play a critical role in defining technical strategy for developing, training, and deploying AI/ML models\u2014particularly in edge ML and NPU enabled platforms\u2014while 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.\n\nKey Responsibilities\n\nTechnical Strategy and Leadership\n\n * Define and guide AI/ML technology strategy across Dolby\u2019s 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\n * Anticipate evolving business and technical needs and contribute to a forward looking technical vision\n * Establish best practices, guardrails, and technical guidelines for building, training, optimizing, and deploying ML models across the organization\n * 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\n\n\n\nBridge Research and Engineering\n\n * Serve as a primary technical interface between ML research teams and engineering teams\n * Define architectural approaches for integrating traditional audio/video processing (DSPs, hardware accelerators) with ML models\n * Partner with platform managers and engineering teams to integrate ML models into shipped products, and collaborate with researchers to align on requirements and constraints\n * Work with Data Engineering teams to help establish data governance guidelines and standards for data sourcing, cleaning, and pipeline management\n * Collaborate with QA teams to develop testing methodologies appropriate for AI/ML systems\n\n\n\nEngage with Silicon Vendors\n\n * Develop a working understanding of GPU and NPU architectures, toolchains, operator support, and performance characteristics\n * Identify gaps between model requirements and hardware capabilities, and help drive solutions in collaboration with internal teams and external partners\n * Collaborate with and influence silicon vendors and platform partners on roadmap alignment, tooling, and hardware capabilities relevant to Dolby use cases\n\n\n\nHands On Technical Work\n\n * Conduct technical investigations and experiments, including profiling models, benchmarking inference, and evaluating accuracy latency trade offs\n * Apply and advise on model optimization techniques such as retraining, pruning, quantization, distillation, and hardware aware optimization\n * Guide model porting across frameworks and runtimes (e.g., PyTorch \u2192 ONNX \u2192 vendor specific runtimes)\n * Build prototypes and proof of concepts to reduce technical risk prior to full engineering investment\n\n\n\nQualifications\n\nRequired\n\n * Bachelor\u2019s or Master\u2019s degree in Electrical Engineering, Computer Science, or a related field, or equivalent practical experience\n * Significant hands on experience in AI, machine learning, and embedded software engineering (often acquired over many years of professional practice)\n * Strong software engineering skills, including experience writing production quality code and working with version control, testing, build systems, and software delivery pipelines\n * Experience with at least one major AI/ML framework (e.g., PyTorch, TensorFlow, JAX, ONNX) and the ability to learn additional frameworks as needed\n * Hands on experience deploying optimized ML models (e.g., quantization, pruning, distillation, operator fusion)\n * Experience with edge or on device ML, including awareness of constraints such as latency, power, memory, and thermal limits\n * Familiarity with CPU, GPU, NPU, and DSP architectures and their associated toolchains (e.g., Qualcomm Hexagon/QNN, MediaTek APU/NeuroPilot, ARM Ethos, Apple Neural Engine)\n * Experience in audio, video, signal processing, media codecs, or closely related technical domains\n\n\n\nStrongly Preferred\n\n * Ability to work across abstraction layers, from model architecture to operator level hardware performance\n * Experience defining technical strategy and influencing cross functional teams through expertise and collaboration\n * Demonstrated experience shipping ML models to production on resource constrained devices (e.g., mobile, embedded, automotive, wearables)\n\n\n\nNice to Have\n\n * Experience with real time audio/video inference pipelines (e.g., streaming inference, causal models, latency sensitive processing)\n * Familiarity with Dolby technologies (such as Atmos, Vision, or AC 4) or comparable media standards\n * Experience with generative AI models in the audio or video domain\n * Contributions to open source ML tools or peer reviewed research\n\n\n\nWhat This Role Is Not\n\n * This is not a pure research role; the focus is on translating research into production ready solutions\n * This is not an MLOps, LLM only, or infrastructure focused role\n * This role does not center on integrating third party APIs; the work involves developing proprietary models\n * This is not a people management role, though the position involves technical leadership and influence\n\n\n\nThe San Francisco/Bay Area base salary range for this full-time position is $152,000 - $209,000, which can vary if outside this location, plus bonus, benefits, and some roles may also include equity. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, competencies, experience, market demands, internal parity, and relevant education or training. Your recruiter can share more about the specific salary range and perks and benefits for your location during the hiring process.\n\n#LI-JB1\n\nDolby will consider qualified applicants with criminal histories in a manner consistent with the requirements of San Francisco Police Code, Article 49, and Administrative Code, Article 12\n\nEqual Employment Opportunity: \nDolby is proud to be an equal opportunity employer. Our success depends on the combined skills and talents of all our employees. We are committed to making employment decisions without regard to race, religious creed, color, age, sex, sexual orientation, gender identity, national origin, religion, marital status, family status, medical condition, disability, military service, pregnancy, childbirth and related medical conditions or any other classification protected by federal, state, and local laws and ordinances.\n