Senior ML Platform Engineer – Scalable Inference

SNAP, Inc.; Los Angeles (CA); On-site; USD 178,000 - 313,000; Full time

Benefits offered by this job

Paid parental leave

Comprehensive medical coverage

Mental health support

Job summary

Snap Inc. is seeking a Software Engineer to join the ML Platform Experience team, part of the core ML Platform organization.

You will design and optimize infrastructure for machine learning workloads at scale, focusing on reliability and efficiency across Snapchat’s ML infrastructure. You will build and enhance data pipelines for online inference and offline training, develop high‑performance model serving, and collaborate with ML engineers to deploy cutting‑edge models.

Qualifications

• Bachelor’s degree in computer science or equivalent experience.
• 6+ years of post-Bachelor’s software development experience or equivalent qualifications.
• Experience building large-scale production machine learning systems and distributed systems.

Responsibilities

• Design and optimize infrastructure for ML workloads at scale.
• Build and enhance feature generation and serving pipelines for online inference and offline training data.
• Develop high-performance inference systems for fast model serving.
• Build infrastructure for scalable ML model training, evaluation, and inference in the cloud.
• Collaborate with ML engineers to deploy cutting-edge models into production.
• Design and ship scalable services with strong code quality, security, and reliability.

Skills

Python

Java

Distributed systems

Big data frameworks

Collaboration

High availability

Learning adaptability

Education

Bachelor’s degree in CS or equivalent

Master’s or PhD (preferred)

Tools

Spark

Flink

Ray

TensorFlow

PyTorch

Job description

Snap Inc. is seeking a Software Engineer to join the ML Platform Experience team, part of the core ML Platform organization.

You will design and optimize infrastructure for machine learning workloads at scale, focusing on reliability and efficiency across Snapchat’s ML infrastructure. You will build and enhance data pipelines for online inference and offline training, develop high‑performance model serving, and collaborate with ML engineers to deploy cutting‑edge models.