Software Engineer 4/5 – Data and Feature Infrastructure, AI Platform
Core
Build a next-generation ML data and feature platform to enable ML practitioners to easily define, test, and access features and labels for high-throughput training and low-latency inference.
Role type
Senior IC machine-learning infrastructure engineer (feature engineering)
Builds
Near-real-time feature computation engines, centralized feature and embedding stores, and data serving infrastructure for ML models.
Domain
Streaming entertainment / Machine Learning Infrastructure
Deliverable
production ML models
Required skills
Large-scale data processing frameworks (Spark, Flink, Kafka), Data storage and serving technologies (Iceberg, Cassandra), Java and/or Python, Public clouds (AWS), Building and operating 24/7 high-traffic online applications, ML or data infrastructure experience
Preferred skills
ML feature stores (Chronon), Embedding-based retrieval systems, Notebooks (Jupyter, Polynote)
Technologies
Spark, Flink, Kafka, Iceberg, Cassandra, Java, Python, AWS, Chronon, Jupyter, Polynote
Responsibilities
Design and build near-real-time feature computation engines; Operate and manage feature computation pipelines and serving infrastructure; Build and scale systems for performant data loading, transformation, and writing; Create frameworks to streamline data availability for training and serving; Develop feature stores for feature discovery and sharing; Increase ML practitioner productivity by simplifying feature and label access.
Seniority
Senior, hands-on IC
