[Hiring] Software Engineer 6 - Feature Infrastructure @Netflix

Role Description

In this role, you will have the opportunity to build a next-generation ML data and feature platform to significantly improve the productivity of ML practitioners. Our goal is to enable our ML practitioners to easily define and test ML features and labels, while our platform takes care of the computation, storage, and serving of feature values for both high-throughput training and low-latency member-scale inference use cases.

You will also have the opportunity to build a centralized feature and embedding store to enable sharing across various ML domains. Unlocking access to these shared datasets will foster innovation through ML in new business areas that otherwise wouldn’t have been feasible. You will collaborate closely with ML practitioners and domain experts to ensure that our models are built with high-quality features and labels. You will also get to work with the broader AI Platform organization to deliver a cohesive end-user experience that significantly improves the productivity of ML practitioners.

Here are some examples of the types of things you would work on:

• Design and build a near-real-time feature computation engine to generate ML features for both high-throughput training and low-latency inference applications.
• Operate and manage the feature computation pipelines and feature serving infrastructure for various ML models across multiple ML domains.
• Build and scale systems that accelerate training through performant data loading, transformation, and writing.
• Create frameworks to streamline and 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 making it easy to define and access features and labels for experimentation and productization.

Qualifications

• 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 with data storage and serving technologies such as Iceberg and Cassandra
• Experience in working with and optimizing Java and/or Python codebases
• Experience with public clouds, especially AWS
• Self-driven and highly motivated team player

Requirements

• Experience in building and operating ML feature stores, such as Chronon
• Experience in building embedding-based retrieval systems
• Experience working with Notebooks such as Jupyter or Polynote

Benefits

• Comprehensive Health Plans
• Mental Health support
• 401(k) Retirement Plan with employer match
• Stock Option Program
• Disability Programs
• Health Savings and Flexible Spending Accounts
• Family-forming benefits
• Life and Serious Injury Benefits
• Paid leave of absence programs
• Full-time hourly employees accrue 35 days annually for paid time off
• Full-time salaried employees are immediately entitled to flexible time off