Senior ML Infrastructure Engineer - Scalable AI Platform

Snap Inc.

Seattle (WA)

On-site

USD 209,000 - 313,000

Full time

14 days+

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Benefits offered by this job

Paid parental leave
Comprehensive medical coverage
Emotional and mental health support
Equity / RSUs

Job summary

Snap Inc. in Seattle is seeking a Software Engineer for the ML Platform to scale ML infrastructure, optimize training and inference, and advance ranking and retrieval systems.

You will design and optimize infrastructure, build scalable ML training and inference pipelines in the cloud, and collaborate with ML engineers to deploy models into production. The role requires strong Python/Java/Scala/C++ skills, plus experience with distributed systems and large-scale ML platforms.

Qualifications

  • Bachelor's degree in computer science or equivalent experience.
  • 6+ years post-Bachelor's software development experience.
  • Experience building large-scale production ML systems and distributed data processing.

Responsibilities

  • Design and optimize infrastructure for ML workloads at scale.
  • Develop high-performance inference systems for fast model serving.
  • Build scalable ML training, evaluation, and inference pipelines in the cloud.
  • Develop data management systems for data collection, labeling, processing, and evaluation.
  • Collaborate with ML engineers to deploy models into production.

Skills

Python
Java
Scala
C++
Distributed systems
Big data

Education

Bachelor's degree in CS or equivalent
6+ years post-Bachelor software dev
MS/PhD in technical field (preferred)

Tools

Spark
Flink
Ray

Job description

Snap Inc. in Seattle is seeking a Software Engineer for the ML Platform to scale ML infrastructure, optimize training and inference, and advance ranking and retrieval systems.

You will design and optimize infrastructure, build scalable ML training and inference pipelines in the cloud, and collaborate with ML engineers to deploy models into production. The role requires strong Python/Java/Scala/C++ skills, plus experience with distributed systems and large-scale ML platforms.

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