Staff Software Engineer, AI Platform

LinkedIn is seeking a Staff Software Engineer to join their AI Platform team, focusing on pushing the boundaries of scaling large models. This is a unique opportunity to work at the intersection of AI infrastructure and large-scale systems at one of the world's largest professional networks.

The role spans multiple critical areas of AI infrastructure development. You'll be working on scaling LinkedIn's AI model training and feature engineering infrastructure, handling models with hundreds of billions of parameters. The team is responsible for optimizing performance across algorithms, AI frameworks, data infrastructure, compute software, and hardware to maximize the potential of their GPU fleet.

As a Staff Engineer, you'll have the opportunity to work closely with the open source community, with many team members being active contributors to projects like TensorFlow, Horovod, Ray, vLLM, and DeepSpeed. The role involves working with cutting-edge technologies including LLMs, GNNs, Incremental Learning, and Online Learning.

The position offers multiple focus areas:

• Model Training Infrastructure: Building next-gen training infrastructure, implementing high-performance data I/O, and enabling distributed training for massive models
• Feature Engineering: Developing state-of-the-art Feature Platform for AI users, working with technologies like Spark, Beam, and Flink
• Model Serving Infrastructure: Creating low-latency applications for serving large complex models, with focus on GPU-based inference
• MLOps: Building infrastructure for MLOps and experimentation systems

The role offers competitive compensation, comprehensive benefits, and the opportunity to work in a hybrid environment. LinkedIn's culture emphasizes trust, connection, and professional growth. You'll be part of a team that values innovation, collaboration, and technical excellence while working on systems that impact millions of users globally.

This is an ideal position for someone who wants to combine deep technical expertise in AI infrastructure with the opportunity to influence the direction of machine learning platforms at scale. The role offers significant autonomy, technical challenges, and the chance to work with cutting-edge AI technologies while contributing to open source communities.