eightfold
Senior Manager, Data & AI Platform
Dolby Laboratories
Bengaluru, IND
Full-time
Senior
Onsite
Discovered 3 weeks ago
SparkRayKubernetesAirflowFlyteMetaflow
Free
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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
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