This role pays more than
94%
of similar roles. Most pay
$194,500–$251,775
— the shaded band above.
At the midpoint, this role pays about
$286k
versus about
$223k
for comparable roles.
Based on 240 similar postings.
Employer
About Snap Inc.
Snap Inc. is a technology and camera company, best known for Snapchat, offering visual communication, augmented reality, and advertising products.
Snap Inc. currently has
61 open roles
on FindRole.
Listed pay typically runs
$209,000–$313,000
across 43 roles with salary data.
As a Staff Software Engineer, ML Infrastructure at Level 6, you will join a high-impact team focused on scaling Snapchat’s machine learning infrastructure. Your role involves designing and optimizing systems for large-scale machine learning workloads to enhance the efficiency of ranking and recommendation systems. Day-to-day responsibilities include developing high-performance embedding generation and batch inference systems, improving data storage and compute efficiency, integrating advanced ML data quality systems, and building comprehensive data management solutions. You will collaborate closely with ML engineers to deploy cutting-edge models into production. The ideal candidate possesses strong programming skills in Python, Java, Scala, or C++, expertise in distributed systems, and experience with big data processing frameworks like Spark, Flink, or Ray. This role demands a deep understanding of large-scale feature stores and embedding systems, as well as familiarity with ML frameworks such as Pytorch and Tensorflow.
Design and optimize ML infrastructure systems for large-scale workloads.
Develop high-performance embedding generation and batch inference systems.
Create efficient data storage and compute solutions for ML tasks.
Integrate advanced ML data quality systems to ensure model performance.
Build scalable data management systems for collection, labeling, and processing.
What we're looking for
Extensive experience (9+ years) in software development, preferably with a focus on large-scale production ML systems or distributed/big data processing.
Strong programming proficiency in Python, Java, Scala, or C++.
Deep understanding of distributed systems and infrastructure components for large-scale machine learning.
Proven ability to design and optimize high-performance embedding generation/batch inference systems.
Expertise in developing efficient data storage/compute systems for ML infrastructure scalability.
Comprehensive knowledge of state-of-the-art ML data quality assurance techniques.