Senior Manager, Data & AI Platform

 At Dolby, we’re changing the way the world experiences sight and sound. We enable people to experience music and movies; videos and pictures in all its intended grandeur and make life & work more meaningful and immersive. We give technology to the world’s content creators, owners, and distributors; manufacturers of TV, Mobile, and PC; and social and media platforms; so that they can truly delight their customers. We're the ones behind the astounding sound and sight experiences in the movie theaters and in your living room; on your mobile phones and on the internet. 

What You Will Do 

We’re building a unified data and AI/ML model development and training platform—similar in ambition to those platforms at other media technology companies—to accelerate our entire AI/ML lifecycle from data preparation and feature computation to experimentation, large-scale distributed training, evaluation, deployment, and governance & observability. 

As Senior Manager, Data & AI Platform, you will lead the team responsible for integrating the data systems, compute orchestration, and AI/ML tooling and exposing these core platform services through high‑quality APIs and SDKs, which hide infrastructure complexity and maximize practitioner velocity across Dolby teams working on audio, video, personalization, content understanding, and generative AI. A major focus is creating horizontal, reusable components—feature pipelines, embedding services, training/evaluation frameworks, SDKs, and model management APIs—to simplify the end‑to‑end AI/ML lifecycle.  

Your work will enable researchers and engineers to move rapidly and safely from early experimentation to production, with standardized workflows, governance, observability, repeatability and reproducibility, and developer productivity built in. 

Key Responsibilities 

• Set a multi‑year platform roadmap that integrates data pipelines, AI/ML workflows, and compute orchestration layers across cloud and on‑prem infrastructure — ensuring seamless experience spanning dataset creation to model training, evaluation, and deployment. 

• Build a unified platform that abstracts complex underlying engines (Spark, Ray, Kubernetes, Airflow/Flyte/Metaflow, etc.) to provide secure, governed, cohesive workflows  

• Deliver robust APIs, SDKs and developer friendly tools and interfaces (CLIs, GUIs, dashboards, etc) to enable AI/ML researchers and engineers to build repeatable, modular, composable end‑to‑end workflows/pipelines — from dataset selection and preparation through model training, evaluation, tuning, and deployment. 

• Ensure that all stages of the AI/ML lifecycle — dataset selection, feature engineering, training, evaluation, tuning and AI/ML artifact management — can be orchestrated via consistent, versioned workflows with full lineage and reproducibility 

• Provide guardrails and governance around data and AI/ML models while enabling researchers to move from exploratory work to production-level pipelines with minimal friction by leveraging common evaluation frameworks and automated checks 

• Partner with platform engineering/MLOps and infrastructure to define observability stacks for metrics, drift indicators, performance regressions, training/inference health signals, production reliability (SLIs/SLOs), monitoring, and incident response. 

• Hire, mentor, and develop architects and senior engineers across data infrastructure and developer experience 

What you need to succeed  

Desired Background 

• BS in Computer Science, Mathematics, Engineering, or equivalent technical field. Master’s Highly preferred. 

• Proven platform leadership delivering integrated Data and AI/ML platforms used at enterprise scale, with deep experience integrating data processing, orchestration, and AI/ML frameworks and tools to deliver consistent, standardized workflows and pipelines 

• Strong API/SDK design experience enabling AI/ML researchers and engineers to build reliable, reusable, and scalable pipelines 

• Deep knowledge and extensive experience with GPU infrastructure, distributed systems, orchestration engines, AI/ML toolchains, query engines, pipeline technologies i.e., Ray, Spark, Metaflow/Flyte/Airflow/Argo/Kubeflow, Kubernetes. 

• Deep expertise across the AI/ML ecosystem tooling such as PyTorch/TensorFlow/Hugging Face, MLOps stack: experiment tracking, feature stores, model registries/lineage (MLFlow, WnB, Feast, etc), and AI/ML-related AWS/GCP managed services (VertexAI, SageMaker, etc) 

• Excellent communication & partnership skills, able to translate complex system design into intuitive developer-facing abstractions 

• Bonus. Familiarity with media technology companies data and AI/ML platform approaches, technologies and open-source contributions 

• Bonus. Knowledge of multimodal AI/ML (audio, video, text) data preparation, feature extraction, model development, training, and evaluation workflows 

• Bonus. Knowledge of LLM/foundation model sizing/estimation, training requirements, evaluation workflows and orchestration and deployment patterns. 

• Bonus. Experience designing feature stores or embedding services tightly integrated with training pipelines.