Staff Software Engineer, AI Platform

LinkedIn, the world's largest professional network, is seeking a Staff Software Engineer to join their AI Platform team. This is a unique opportunity to work on cutting-edge AI infrastructure that powers LinkedIn's machine learning capabilities across recommendation systems, large language models, and computer vision applications.

The role can be based in Mountain View, CA, San Francisco, CA, or Bellevue, WA, with a hybrid work arrangement that combines remote work with office presence. The team is at the forefront of scaling AI model training and serving, working with models containing hundreds of billions of parameters and managing large-scale feature engineering infrastructure.

As a Staff Software Engineer, you'll be involved in three key areas:

1. Model Training Infrastructure: You'll build next-generation training infrastructure, optimize data I/O, collaborate with open source teams on libraries like Huggingface and PyTorch, and enable distributed training for massive models. You'll also work on containerized pipeline orchestration and maintain deep learning frameworks.

2. Feature Engineering: You'll help develop the state-of-the-art Feature Platform that handles feature creation, computation, storage, and governance across online, offline, and nearline environments. This involves working with technologies like Spark, Beam, and Flink to process data at scale.

3. Model Serving Infrastructure: You'll build high-performance applications for serving large and complex models, including LLMs and personalization models. This involves GPU-based inference optimization and handling significant scale requirements.

The role offers excellent growth opportunities, including working with talented researchers and engineers while building your career in the AI industry. LinkedIn provides competitive compensation, comprehensive benefits, and a culture focused on well-being and professional development.

The ideal candidate will have strong experience in distributed systems, machine learning infrastructure, and a track record of technical leadership. This is a chance to shape the future of AI infrastructure at one of the world's leading professional networks while working with cutting-edge technologies and open-source projects.