AI Platform Engineer - Scalable ML Infra

LinkedIn; Mountain View (CA); Hybrid; USD 120,000 - 195,000; Full time

Job summary

LinkedIn is seeking an engineer for its AI Training Infra team to design high-performance data I/O, enable distributed training across hundreds of billions of parameters, and optimize deep learning pipelines. You will work with PyTorch, TensorFlow, and other libraries, while supporting internal AI teams with advanced tooling.

The role focuses on scalable infrastructure, model parallelism, container orchestration, and GPU-based inference, in a hybrid work setting that blends home and office days.

Qualifications

• Bachelor’s Degree in Computer Science or related technical discipline, or equivalent practical experience
• 1+ years in industry with building deep learning systems
• Experience with Java, C++, Python, Go, Rust, C# and/or Scala or other relevant languages
• Experience, qualifications in Machine Learning, AI

Responsibilities

• Designing, implementing, and optimizing the performance of large-scale distributed serving or training for personalized recommendation as well as large language models
• Improving the observability and understandability of various systems with a focus on developer productivity
• Partner with peers to define, scope, prioritize, and build impactful features at high velocity

Skills

Distributed systems

Java

C++

Python

Go

Rust

ML/AI knowledge

Education

Bachelor’s Degree in Computer Science or related technical discipline

Tools

PyTorch

TensorFlow

HuggingFace

CUDA/CuDNN

NCCL

Kubernetes

Job description

LinkedIn is seeking an engineer for its AI Training Infra team to design high-performance data I/O, enable distributed training across hundreds of billions of parameters, and optimize deep learning pipelines. You will work with PyTorch, TensorFlow, and other libraries, while supporting internal AI teams with advanced tooling.

The role focuses on scalable infrastructure, model parallelism, container orchestration, and GPU-based inference, in a hybrid work setting that blends home and office days.