Job Description

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 Senior Software Engineer, you will design and implement core platform services exposed through high‑quality APIs and SDKs for a platform that seamlessly integrates data systems, compute orchestration, and AI/ML tooling. These abstractions 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:

• Design and build platform primitives—Python SDKs, platform APIs, and templates—that enable reproducible experiments, configuration-as-code workflows, model lineage, and artifact tracking, which enable seamless promotion from research to production.
• Create developer tools to elevate development experience—CLIs, UI, dashboards, visualization layers—that simplify platform operation and multi-stage workflows.
• Implement and scale distributed training systems (multi-node GPU workloads) on top of Kubernetes and cloud-based orchestration foundation.
• Build large-scale evaluation frameworks for offline tests, shadow deployments, and A/B experimentation.
• Implement model/dataset versioning, approvals, lineage tracking, retention, and compliance hooks.
• Partner with AI/ML research, platform engineering/MLOps and infrastructure, and data engineering teams to generalize workflows into reusable frameworks.
• 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.

What you need to succeed

Desired Background:

• BS in Computer Science, Mathematics, Engineering, or equivalent technical field. Master’s preferred.
• Proven track record building 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
• Extensive hands-on experience with Kubernetes (EKS, GKE, self-hosted, etc) including autoscaling and job scheduling frameworks, GPU infrastructure, and AI/ML-related AWS/GCP managed services (VertexAI, SageMaker, etc).
• Deep expertise with AI/ML ecosystem and tooling such as PyTorch, TensorFlow, Ray, experiment/feature/model stores (MLFlow, WnB, Feast, etc), Hugging Face
• Proven ability to scale AI/ML workloads and pipelines—pipeline SDKs, feature/model CI/CD, automated evaluation, safe rollouts, monitoring
• Strong developer-experience mindset: ability to translate research/engineering friction into elegant APIs, templates, and tools that reduce time-to-first-successful remote run and raise platform adoption.

Preferred Skill:

• Previous experience with Databricks.
• Knowledge of multimodal AI/ML (audio, video, text) data preparation, feature extraction, model development, training, and evaluation workflows.
• Experience with LLM/foundation model sizing/estimation, training requirements, pipelines, and deployment.
• Knowledge of LLM/foundation model sizing/estimation, training requirements, evaluation workflows and orchestration and deployment patterns.
• Experience designing feature stores or embedding services tightly integrated with training pipelines.

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