Senior ML Infrastructure Engineer (Cloud-Scale)

Snap; Palo Alto (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Snap Inc is seeking a Software Engineer to join the ML Platform team. You will design and optimize infrastructure for ML workloads, build fast inference, and enable scalable cloud-based training and evaluation of models.

The role requires strong programming in Python/Java/Scala/C++, plus distributed systems experience and familiarity with Spark, Flink, or Ray. You will collaborate with ML engineers to deploy models into production and help manage large-scale data pipelines.

Qualifications

• Bachelor's degree in a technical field such as computer science or equivalent experience.
• 6+ years of post-Bachelor's software development experience; or Master's with 5+ years; or PhD with 2+ years.
• Experience building large-scale production machine learning systems, distributed systems or big data processing.

Responsibilities

• Design and optimize infrastructure systems for machine learning workloads at scale.
• Develop high-performance inference systems for fast model serving.
• Build infrastructure for scalable ML model training, evaluation, and inference in the cloud.
• Develop data management systems for scalable data collection, labeling, processing, and evaluation.
• Explore vector search algorithms to improve retrieval system scalability and precision.
• Collaborate with ML engineers to deploy models into production.

Skills

Python

Java

Scala

C++

Distributed systems

Big data frameworks

Collaboration

Operations at scale

Education

Bachelor's degree in Computer Science or related field

Master's degree in Computer Science (preferred)

PhD in CS (preferred)

Tools

Spark

Flink

Ray

Job description

Snap Inc is seeking a Software Engineer to join the ML Platform team. You will design and optimize infrastructure for ML workloads, build fast inference, and enable scalable cloud-based training and evaluation of models.

The role requires strong programming in Python/Java/Scala/C++, plus distributed systems experience and familiarity with Spark, Flink, or Ray. You will collaborate with ML engineers to deploy models into production and help manage large-scale data pipelines.