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 t

eam.

Join u s to push the boundaries of scaling large models toge ther. 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 focused on technologies like LLMs, GNNs, Incremental Learning, Online Learning and Serving performance optimizations across billions of user que

**ries.

Model Training Infrastru** cture: As an engineer on the AI Training Infra t eam, 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 Infrastru** cture: 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. Staff Software En gineer, you will have first-hand opportunities to advance one of the most scalable AI platforms in th e 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 i

**ndustry.

Responsi**

• bilities:

  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 langua

• ge models.Improving the observability and understandability of various systems with a focus on improving developer productivity and system s

• ustenance.Mentoring other engineers, defining our challenging technical culture, and helping to build a fast-gro

• wing team.Working closely with the open-source community to participate and influence cutting edge open-source projects (e.g., vLLM, PyTorch, GNNs, DeepSpeed, HuggingFa

• ce, etc.).Functioning as the tech-lead for several concurrent key initiatives AI Infrastructure and defining the future of AI

**Platforms.

Qual**

• ifications:

  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 leaders

• hip position10+ years of experience in an object-oriented programming language such as Python, C++, Java, Go,

• Rust, Scala5+ years of experience with large-scale distributed systems and client-server a

• rchitecturesExperience building ML applications, LLM serving,

• GPU serving.Co-author or maintainer of any open-sou

• rce projectsExpertise in machine learning infrastructure, including technologies like MLFlow, Kubeflow and large-scale distrib

• uted systemsExpertise in deep learning frameworks and tensor libraries like PyTorch, Tensorfl

**ow, JAX/FLAX

Sugg**

• ested Skills:

  ML Algorit

• hm DevelopmentMachine Learning and

• Deep LearningInformation retrieval / recommen

• dation systemsTechni

cal leadership

LinkedIn is committed to fair and equitable compensa

tion practices.The pay range for this role is $198,000 to $326,000. 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. This may be different in other locations due to differences in the

cost of labor.

The total compensation package for this position may also include annual performance bonus, stock, benefits and/or other applicable incentive compensation plans. For more information, visit https://careers.linked

**in.com/benefits.

Addit**

ional Info rmation

Team Name: The Search and Fe

ed Federation Team

Team Description: The Search & Feed Federation team is hiring a Sr. Staff Software Engineer to lead the design and development of a next-generation, high-performance orchestrator powering LinkedIn’s Search and Feed systems. Operating at massive scale (100K–1M+ QPS, Tier-1 availability), this role is central to delivering fast, relevant, and reliable content to ever

y LinkedIn member.

This engineer will define and drive the technical vision for a highly extensible federation platform, establish clear API and contract boundaries with product and business teams, and guide the architecture across multiple orgs. The first six months will emphasize understanding requirements, creating the system vision, and driving foundational architectural work, followed by meaningful hands-on engineering and ope

rational ownership.

Success requires deep experience building P0/P1 platform infrastructure and orchestrators at large-scale companies, strong architectural leadership, and the ability to influence senior stakeholders across Search, Feed, and Infrastructure teams. This role partners closely with existing Senior Staff/P-Staff engineers to shape the future of LinkedIn’s core conte

**nt delivery systems.

Equal O**

pportunity Statement We seek candidates with a wide range of perspectives and backgrounds, and we are proud to be an equal opportunity employer. LinkedIn considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other leg

**ally protected class.

Pay Transpar**

ency Policy Statement As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://ln

**kd.in/paytransparency.

Global Data Privacy Not**

ice for Job Candidates Please follow this link to access the document that provides transparency around the way in which LinkedIn handles personal data of employees and job applicants: https://legal.linkedi

n.com/ca

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