Senior Manager, Foundational AI Research

What You Will Do We are building Foundational AI capabilities that advance how people create, understand, enhance, deliver, and experience media. This work spans foundation models, multimodal AI, generative media, content intelligence, media enhancement, personalization, and applied AI systems that can be reused and adapted across Dolby initiatives. As Senior Manager, Foundational AI, you will lead the team responsible for defining and delivering Dolby s Foundational AI strategy across audio, video, imaging, and multimodal experiences. You will set the technical roadmap for reusable AI capabilities, model assets, training and adaptation methods, evaluation frameworks, and reference implementations that help Dolby teams move from research exploration to practical adoption. This role requires strong technical leadership in modern AI/ML systems, deep judgment in model strategy, evaluation methodology, and applied AI development, and the ability to connect research advances with practical product and business opportunities. You will work closely with research, product, engineering, platform, legal, privacy, data, and business teams to ensure Dolby s Foundational AI investments are differentiated, responsibly developed, technically rigorous, and broadly adoptable across the company. A major focus of this role is building reusable AI assets and capabilities, including pretrained models, fine-tuning and adaptation recipes, evaluation methodologies, training workflows, model quality benchmarks, and reference implementations. These capabilities will help Dolby teams develop new experiences more efficiently across media understanding, media enhancement, AI-assisted creation, immersive content, personalization, and next-generation entertainment workflows. Your work will help researchers and engineers move from early exploration to validated, reproducible, and product-relevant AI systems, with clear quality standards, evaluation discipline, responsible AI practices, and practical adoption paths built in. Key Responsibilities Define and drive a multi-year roadmap for Dolby s Foundational AI strategy across audio, video, imaging, multimodal AI, generative media, content understanding, media enhancement, personalization, and immersive experiences. Lead the development of reusable Foundational AI capabilities, including pretrained models, adaptation and fine-tuning recipes, training workflows, evaluation benchmarks, and reference implementations that can be adopted across multiple Dolby teams. Set technical direction for model architecture, representation learning, self-supervised learning, generative modeling, multimodal learning, model adaptation, optimization, and evaluation, with attention to quality, robustness, efficiency, scalability, and product relevance. Establish model development and evaluation strategies for Foundational AI, including training and validation requirements, data quality expectations, benchmark design, objective metrics, perceptual evaluation, human studies, robustness testing, and product-specific quality gates. Partner with product, business group, research, engineering, data, and platform teams to identify high-impact use cases and translate Foundational AI capabilities into practical applications across Dolby products, services, and partner-facing experiences. Drive research-to-product transition by moving promising prototypes into validated, reproducible, scalable, and adoptable solutions with clear quality standards, responsible AI practices, and enterprise-ready governance. Work closely with platform, MLOps, infrastructure, and data teams to ensure Foundational AI development has the right compute, data pipelines, experiment tracking, artifact management, observability, and deployment paths. Hire, mentor, and develop a high-performing team of research scientists while building a culture that balances research creativity, responsible AI practices, and measurable product impact. What You Need to Succeed Required Qualifications BS, MS, or PhD in Computer Science, Electrical Engineering, Machine Learning, Applied Mathematics, Physics, or a related technical field. Advanced degree preferred. Proven experience leading and scaling AI/ML teams focused on foundation models, multimodal AI, generative AI, media AI, or applied machine learning systems. Strong technical depth in modern AI/ML, including model architecture, representation learning, self-supervised learning, fine-tuning, model adaptation, evaluation, and deployment considerations. Experience leading model development across one or more modalities such as audio, speech, music, video, image, text, or multimodal systems. Strong understanding of model development workflows, including training and validation data requirements, data quality, evaluation design, governance, privacy, licensing considerations, and continued learning. Experience defining evaluation methodologies for complex AI/ML systems, including offline metrics, human evaluation, perceptual quality assessment, robustness testing, and product quality gates. Ability to connect research direction with product and business impact, and to prioritize AI investments based on technical feasibility, differentiation, adoption potential, and customer value. Experience partnering with platform, MLOps, infrastructure, product, legal, privacy, data, and business stakeholders to move AI/ML capabilities from research into production. Strong communication skills, with the ability to explain AI strategy, technical tradeoffs, risks, roadmap decisions, and business implications to technical and executive audiences. Demonstrated ability to hire, mentor, and grow senior research talent. Preferred Qualifications Hands-on experience with modern deep learning frameworks and ecosystems such as PyTorch, TensorFlow, JAX, Hugging Face, Lightning, DeepSpeed, FSDP, or related technologies. Experience with foundation model training, fine-tuning, instruction tuning, model compression, distillation, quantization, efficient inference, or large-scale distributed training. Experience developing or applying transformer models, diffusion models, autoregressive models, contrastive learning, multimodal representation learning, or generative models. Familiarity with production deployment patterns for AI/ML systems, including cloud inference, edge inference, model serving, model monitoring, and continuous improvement loops. Familiarity with content creation workflows, post-production workflows, streaming platforms, creator tools, entertainment ecosystems, or partner-facing AI/ML development. Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.