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Senior AI/ML Engineer
Dolby Laboratories, Inc.
Bengaluru, IND
Full-time
Senior · 5+ years experience
Hybrid
Discovered 1 weeks ago
PythonScalaJavaDatabricksDelta LakeApache Spark
Free
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Role Overview
Dolby's Data & AI Platform team builds the systems that powers machine learning and analytics across the company, from codec telemetry spanning billions of devices to the media pipelines behind Dolby Atmos and Dolby Vision. Our data is high-volume, real-time, and deeply tied to how people experience sound and image worldwide.
We're hiring a Senior AI Engineer to design and own the data pipelines, platform services, and ML systems that our researchers, data scientists, and product teams depend on daily. You'll work across the full lifecycle, from ingestion through model deployment on systems that run at scale in production Kubernetes and Databricks environments. This is a hands-on engineering role with real ownership. You'll build things, put them in production, and keep them running.
Key Skills for This Role
PythonScalaJavaDatabricksDelta LakeApache Spark
Full Job Posting
About the Role
Dolby's Data & AI Platform team builds the systems that powers machine learning and analytics across the company, from codec telemetry spanning billions of devices to the media pipelines behind Dolby Atmos and Dolby Vision. Our data is high-volume, real-time, and deeply tied to how people experience sound and image worldwide.
We're hiring a Senior AI Engineer to design and own the data pipelines, platform services, and ML systems that our researchers, data scientists, and product teams depend on daily. You'll work across the full lifecycle, from ingestion through model deployment on systems that run at scale in production Kubernetes and Databricks environments. This is a hands-on engineering role with real ownership. You'll build things, put them in production, and keep them running.
What You'll Do
Build and operate ML-ready data systems. Create the data preparation, feature generation, and training pipelines that AI researchers and ML engineers use to take models from experiment to production. Own data versioning, validation, and reproducibility for ML workflows.
Deploy and support production AI/ML systems. Build and maintain the pipelines that serve model training, testing, validation, deployment, and inference in production. Partner with ML engineers to operationalize models reliably.
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