Sr. Staff Software Engineer, AI Infrastructure

About the role

This role will be based in Mountain View, CA or Bellevue, WA. The work location is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by business needs. Join us to push the boundaries of scaling large models together. The team is responsible for scaling LinkedIn's AI model training, feature engineering and serving with hundreds of billions of parameters models and large scale feature engineering infra for all AI use cases from recommendation models, large language models, to computer vision models. We optimize performance across algorithms, AI frameworks, data infra, compute software, and hardware to harness the power of our GPU fleet with thousands of latest GPU cards. The team also works closely with the open source community and has many open source committers (TensorFlow, Horovod, Ray, vLLM, Huggingface, DeepSpeed etc.) in the team. Additionally, this team focussed on technologies like LLMs, GNNs, Incremental Learning, Online Learning and Serving performance optimizations across billions of user queries.

Responsibilities

• Owning the technical strategy for broad or complex requirements with insightful and forward-looking approaches that go beyond the direct team and solve large open-ended problems.
• 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 improving developer productivity and system sustenance.
• Mentoring other engineers, defining our challenging technical culture, and helping to build a fast-growing team.
• Working closely with the open-source community to participate and influence cutting edge open-source projects (e.g., vLLMs, PyTorch, GNNs, DeepSpeed, Huggingface, etc.).
• Functioning as the tech-lead for several concurrent key initiatives AI Infrastructure and defining the future of AI Platforms.

Qualifications

Basic Qualifications:

• BS/BA in Computer Science or related technical field or equivalent technical experience
• 5+ years of industry experience in software design, development, and algorithm related solutions
• 5+ years of experience programming in object-oriented languages such as Python, C++, Java, Go, Rust, Scala
• 2+ years of experience as an architect, or technical leadership position
• 5+ years of experience in the industry with leading / building deep learning systems
• Hands-on experience developing distributed systems or other large-scale systems

Preferred Qualifications:

• MS or PhD in Computer Science or related technical discipline.
• 10+ years of experience in software design, development, and algorithm related solutions with at least 5 years of experience in a technical leadership position
• 10+ years of experience in an object-oriented programming language such as Python, C++, Java, Go, Rust, Scala
• 5+ years of experience with large-scale distributed systems and client-server architectures
• Experience building ML applications, LLM serving, GPU serving.
• Co-author or maintainer of any open-source projects
• Expertise in machine learning infrastructure, including technologies like MLFlow, Kubeflow and large scale distributed systems
• Expertise in deep learning frameworks and tensor libraries like PyTorch, Tensor