In this role at Netflix, you will build a next-generation machine learning data and feature platform aimed at improving the productivity of ML practitioners. You will collaborate with ML practitioners and domain experts to ensure high-quality features and labels are used in models. The focus is on creating a centralized feature and embedding store to foster innovation across various ML domains. This position offers the opportunity to work on cutting-edge data infrastructure that supports personalized recommendations, payments, and more.

• Experience in building ML or data infrastructure
• Strong empathy and passion for providing a fantastic user experience to ML practitioners
• Experience in building and operating 24/7 high-traffic and low-latency online applications
• Experience with large-scale data processing frameworks such as Spark, Flink, and Kafka
• Experience in working with and optimizing Scala and/or Python codebases
• Experience with public clouds, especially AWS
• Self-driven and highly motivated team player

• Design and build a near-real-time feature computation engine for ML features.
• Operate and manage feature computation pipelines and serving infrastructure for various ML models.
• Build and scale systems that accelerate training through efficient data loading and transformation.
• Create frameworks to expedite the availability of new data for training and serving.
• Develop feature stores that enable feature discovery and sharing.
• Increase the productivity of ML practitioners by simplifying access to features and labels.

• Employees at Netflix are often offered flexible, people-first benefits—unlimited time away, generous parental leave, global family-forming support, mental-health programs (mindfulness, free counseling/coaching), and health coverage tailored by country. Financially, Netflix pays at personal top-of-market and lets employees choose their mix of cash vs. fully-vested 10-year stock options, alongside donation and volunteer matching. Convenience perks can include trust-based travel/expense policies, relocation support, and “Work, Not Drive” rideshare flexibility.