At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business. The work location of this role is hybrid, meaning it will be performed both from home and from a LinkedIn office on select days, as determined by the business needs of the team. 

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, Hugginface, 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.

Model Training Infrastructure: As an engineer on the AI Training Infra team, you will play a crucial role in building the next-gen training infrastructure to power AI use cases. You will design and implement high performance data I/O, work with open source teams to identify and resolve issues in popular libraries like Huggingface, Horovod and PyTorch, enable distributed training over 100s of billions of parameter models, debug and optimize deep learning training, and provide advanced support for internal AI teams in areas like model parallelism, tensor parallelism, Zero++ etc. Finally, you will assist in and guide the development of containerized pipeline orchestration infrastructure, including developing and distributing stable base container images, providing advanced profiling and observability, and updating internally maintained versions of deep learning frameworks and their companion libraries like Tensorflow, PyTorch, DeepSpeed, GNNs, Flash Attention. PyTorch Lightning and more and more.

Model Serving Infrastructure: this team builds low latency high performance applications serving very large & complex models across LLM and Personalization models. As an engineer, you will build compute efficient infra on top of native cloud, enable GPU based inference for a large variety of use cases, cuda level optimizations for high performance, enable on-device and online training. Challenges include scale (10s of thousands of QPS, multiple terabytes of data, billions of model parameters), agility (experiment with hundreds of new ML models per quarter using thousands of features), and enabling GPU inference at scale.

As a Sr. Software Engineer, you will have first-hand opportunities to advance one of the most scalable AI platforms in the world. At the same time, you will work together with our talented teams of researchers and engineers to build your career and your personal brand in the AI industry.

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.

Basic Qualifications

• Bachelor's Degree in Computer Science or related technical discipline, or equivalent practical experience
• 2+ years of experience in the industry with leading/ building deep learning systems.
• 2+ years of experience with Java, C++, Python, Go, Rust, C# and/or Functional languages such as Scala or other relevant coding languages
• Hands-on experience developing distributed systems or other large-scale systems.

Preferred Qualifications

• BS and 5+ years of relevant work experience, MS and 4+ years of relevant work experience, or PhD and 2+ years of relevant work experience
• Previous experience working with geographically distributed co-workers.
• Outstanding interpersonal communication skills (including listening, speaking, and writing) and ability to work well in a diverse, team-focused environment with other SRE/SWE Engineers, ---Project Managers, etc.
• Experience building ML applications, LLM serving, GPU serving.
• Experience with distributed data processing engines like Flink, Beam, Spark etc., feature engineering,
• Experience with search systems or similar large-scale distributed systems
• Expertise in machine learning infrastructure, including technologies like MLFlow, Kubeflow and large scale distributed systems
• Co-author or maintainer of any open-source projects
• Familiarity with containers and container orchestration systems
• Expertise in deep learning frameworks and tensor libraries like PyTorch, Tensorflow, JAX/FLAX

Suggested Skills

• ML Algorithm Development
• Experience in Machine Learning and Deep Learning
• Experience in Information retrieval / recommendation systems / distributed serving / Big Data is a plus.

LinkedIn is committed to fair and equitable compensation practices.   

The range of expected compensation for this position is $139,000 to $229,000, which includes potential compensation under non-discretionary annual performance bonus and/or other applicable incentive compensation plans. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to skill set, depth of experience, certifications, and specific work location, and may be subject to the terms and conditions of various applicable plans and policies. Actual compensation packages may be different in other locations due to differences in the cost of labor.   

This position may also include stock grants and/or benefits. For more information, visit https://careers.linkedin.com/benefits. 

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