Responsibilities

• Create systems that enable external and internal data integrations with the media ML platform.
• Collaborate with the Media ML Scientists to identify and implement enhancements that boost the efficiency of research experimentation and product development.
• Operate services while working with the team to enhance procedures that maintain our systems’ stability.
• Define, prototype and implement platform solutions that meet the requirements of ML researchers.
• Develop conceptual models and tools that represent Netflix asset lifecycles within the framework of ML workflows.
• Collaborate with cross-functional teams, including product managers, engineers and data scientists, to define problem statements, roadmaps and execute on priorities.
• Communicate complex concepts and the results of analyses with both technical and non-technical stakeholders to guide strategic decision-making.
• Interact with the internal and external ML community to learn, educate, and contribute towards establishing a strong Netflix presence in the ML domain.

About you

• Bachelor degree in Computer Science, Electrical Engineering, or a related technical field with a minimum of 5 years’ experience in ML infrastructure. As a plus, experience with Media Machine Learning Infrastructure.
• Proven experience in large-scale build, release, continuous integration/continuous deployment (CI/CD), and monitoring methods, specifically in multi-language settings with Scala, Java, and Python.
• Experience (or strong interest in) designing developer tools, CLIs and APIs that interact with complex user workflows including data and auth systems, notebooks & IDEs, compute orchestration and other resource-intensive backend applications.
• Excellent verbal and writing communication skills while explaining complex details to both technical and non-technical audiences.
• Exceptional teamwork abilities, and contributes positively to the team dynamics and company culture, making pragmatic decisions and demonstrating the ability to ’disagree and commit’ when required.
• Advocates for operational best practices and has experience in setting up essential observability, logging, reporting, and on-call procedures to uphold superior engineering standards. 
• Enthusiasm about delving into and broadening your scope on ML-oriented projects. When a project demands profound technical knowledge in a specific area, you can quickly acquire the necessary expertise. When projects necessitate a wide-ranging focus, you are keen to do whatever it takes to deliver value, even if it means stepping out of your comfort zone.

More information

• To learn more about the work we do to check if it align with your interests, check the following articles:
• Scaling Media Machine Learning at Netflix
• Building In-Video Search