Software Engineer L4/L5 - Data and Feature Infrastructure, Machine Learning Platform
Netflix
USA, United States
Base: $466,000.00 - $750,000.00; bonus/equity: no ...
**
Experience building ml or data infrastructure
Experience with spark, flink, and kafka
Strong scala and/or python coding skills
**
Netflix is seeking a Software Engineer (L4/L5) to enhance their Machine Learning Platform by building scalable data and feature infrastructure. The role focuses on improving the productivity of ML practitioners through innovative data solutions while fostering collaboration across various ML domains.
**
Job Summary
The role focuses on building a next-generation ML data and feature platform to significantly improve the productivity of ML practitioners.
You will design and build a near-real-time feature computation engine capable of handling both high-throughput training and low-latency inference applications.
Netflix offers a unique compensation structure where employees choose their salary versus stock options split annually within a market range.
Matching Summary
Match Score: 75
**
Netflix is seeking a Software Engineer (L4/L5) to enhance their Machine Learning Platform by building scalable data and feature infrastructure. The role focuses on improving the productivity of ML practitioners through innovative data solutions while fostering collaboration across various ML domains.
**
Salary
Base: $466,000.00 - $750,000.00; Bonus/Equity: No bonuses; Salary vs Stock choice available; Benefits: Comprehensive health plans, 401(k) match, flexible time off
Skills & Requirements
Must-have
Experience building ML or data infrastructure
Experience with Spark, Flink, and Kafka
Strong Scala and/or Python coding skills
AWS public cloud experience
High-traffic low-latency application operations
Nice-to-have
Experience building ML feature stores
Functional programming background
Notebook experience like Jupyter or Polynote
Empathy for ML practitioner user experience
Key Requirements
Experience in building ML or data infrastructure
Experience working with large-scale data processing frameworks