Senior ML Ops Engineer

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.

• Troubleshooting high-performance computing, storage and networks for machine-learning workloads.

• Collaborate with research, development and engineering to establish machine-learning and data management workflows and supporting tools and processes that maximize machine-learning activities and use of resources.

• Improve capabilities of data set exploration, transformation and overall data management of large to very large datasets.

• Collaborate with research and development to proactively iterate and fine-tune model training for best performance and efficient use of machine-learning resources.

• Collaborate with infrastructure teams physical compute, storage and network infrastructure experts to improve on-premise and cloud infrastructure.

• Improve use of cloud compute and storage for global research teams and manage within budget.

• BS or MS degree in Computer Science or equivalent experience.

• 4+ years of professional practical hands-on experience in machine learning operations or equivalent.

• Comprehensive knowledge of AWS and infrastructure-as-code techniques.

• Advanced proficiency with Python, Terraform, Cloud Formation, Ansible, git and related.

• Experience with machine learning and scaling workloads with both cloud and on-premise GPU server environments.

• Experience with managing and coordinating storage of large machine learning data sets.

• Proficiency in Kubernetes cluster design, deployment and management.

• Interest and understanding of industry trends in machine learning development techniques and tools and processes.

• Comprehensive knowledge of continuous integration and continuous release processes and tools

• Exceptional understanding and practical experience in software and infrastructure configuration management with high-performance compute and storage and maximizing high-availability.

• Active collaborator to help build positive community with researchers, scientists and engineers around machine-learning operations and resources.

• AWS resource management and provisioning.