Staff Architect, Data & AI Platform

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'll 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're 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.

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'll 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 a Staff Architect, you will lead the architectural vision for a platform that seamlessly integrates data systems, compute orchestration, and AI/ML tooling through core platform services exposed 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. Inspired by integrated Data and AI/ML platforms that excel in standardized workflows, developer-friendly integrations, evaluation rigor, and observability, this role ensures Dolby's internal teams can innovate without reinventing foundational infrastructure.

You'll partner closely with AI/ML researchers/engineers, data engineers, and product teams to deliver a paved-path developer experience: from a laptop run to multi-node, GPU-accelerated training; experiment tracking & lineage; model packaging with evaluation hooks; and continuous delivery to production.

Key Responsibilities

• Architect a cohesive, integrated platform that integrate underlying data and AI/ML compute orchestration systems (Kubernetes, Ray, Spark), feature/experiment/model stores, and enable batch, distributed training, and multi-stage pipelines while ensuring consistent resource usage patterns.

• Establish unified pipeline definitions for dataset preparation flows, reliable, scalable training and inference routines, and evaluation workloads to enable reproducibility, repeatability and reuse

• Define stable, well-versioned APIs and Python SDKs, which follow common architectural patterns and provide framework-native integrations (PyTorch/Ray, etc.), to enable teams to build declarative AI/ML workflows spanning dataset selection, preparation, training, tuning, evaluation, and model registration/deployment/serving.

• Design and evolve the experimentation, model checkpointing/registration, and other AI/ML metadata systems to support lineage, observability, lineage, and evaluation to standardize how teams track, assess, compare, and promote models.

• Define best practices for AI/ML version control, build/test/evaluate/tune CI/CD, continuous training, and dependency management

• Collaborate with AI/ML researchers, engineers, platform engineering/MLOps and infrastructure, security, and product stakeholders to align roadmap priorities and architectural sequencing with workflow pain points.

• 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.

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

• Proven experience architecting large-scale distributed systems and integrated data and AI/ML platforms (e.g., training, serving, workflow orchestration, data pipelines).

• Expert-level proficiency in Python and one of Go/Java/C++ and building production-grade services/APIs/SDKs

• 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 with 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)

• Proven ability to scale AI/ML platforms emphasizing reproducibility and repeatability by integrating data preprocessing, feature access, and model training/evaluation routines through workflow orchestration/pipelines, developer-oriented SDKs, feature/model CI/CD, automated evaluation, safe rollouts, and monitoring

• Strong collaboration skills across research, engineering, MLOps, infrastructure, and product; ability to translate cross-functional needs into scalable platform abstractions.

• Bonus. Previous experience with Databricks

• 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.