Job Description

LinkedIn is the world’s largest professional network, built to help members of all backgrounds and experiences achieve more in their careers. Our vision is to create economic opportunity for every member of the global workforce. Every day our members use our products to make connections, discover opportunities, build skills and gain insights. We believe amazing things happen when we work together in an environment where everyone feels a true sense of belonging, and that what matters most in a candidate is having the skills needed to succeed. It inspires us to invest in our talent and support career growth. Join us to challenge yourself with work that matters. 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 AI. The AI Infra team is responsible for scaling LinkedIn’s AI model training, feature engineering and serving with hundreds of billions of parameters models 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. In this role, you will be responsible for implementing and deploying AI models on the Field Programmable Field Array (FPGA) hardware. You will integrate FPGAs in our current CPU and GPU fleet, and develop high-performance and power-efficient FPGA solutions for the large scale AI models. As a Sr. Staff Engineer on the AI Infra team, you will play a crucial role in building the next-gen AI inference infrastructure. You will design and implement FPGA kernels in High Level Specification (HLS) or Hardware Description Language (Verilog, VHDL) and the host code to connect with the kernels. You will be responsible for the kernel development lifecycle (including but not limited to synthesis, place-and-route, timing analysis, validation and verification) and deploy them on hardware platforms (such as AMD Alveo) in LinkedIn data centers. You will develop toolchains to generate FPGA kernels from high-level AI frameworks such as PyTorch, Tensorflow, JAX. You will also develop tooling for monitoring and observability of AI models running on FPGA. This role gives you the unique opportunity to work with a wide spectrum of AI practitioners, AI Infra, Compute Infra, and Data Center Infra teams. Responsibilities Deliver impact by driving innovation while building and shipping software at scale Design, implement, and optimize the performance of large-scale AI models on FPGA for personalized recommendation, large language models, and video models. Improve the observability and understandability of the FPGA fleet with a focus on improving developer productivity and system sustenance. Provide architectural guidance on modern FPGA platforms, tools, and workflows to up-level the engineering organization Mentor other engineers, define our challenging technical culture, and help to build a fast-growing team. Function as the tech-lead for several concurrent key initiatives AI Infrastructure and defining the future of AI Platforms. Basic Qualifications:  BS/BA in Computer Science or related technical field or equivalent technical experience 5+ years of industry experience in hardware design, software design, and algorithm related solutions 5+ years of experience programming in HLS or HDL languages such as C++, SystemC, Verilog, VHDL; and software languages such as Python 5+ years of experience with FPGA development software such as AMD Vitis and Vivado, Intel Quartus. 2+ years of experience as an architect, or technical leadership position Hands-on experience developing FPGA hardware solutions and deployment at scale. Familiarity with deep learning frameworks and tensor libraries like PyTorch, Tensorflow, JAX/FLAX Preferred Qualifications: BS and 8+ years of relevant work experience, MS and 7+ years of relevant work experience, or PhD and 4+ years of relevant work experience 2+ years of experience in hardware acceleration of AI/ML models Experience in deep learning frameworks and tensor libraries like PyTorch, Tensorflow, JAX/FLAX Familiarity with building ML applications, LLM serving, GPU serving. Familiarity with containers and container orchestration systems 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. Suggested Skills: FPGA development AI/ML hardware acceleration Systems Infrastructure LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $180,000 to $300,000. 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 Equal Opportunity 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 legally protected class. LinkedIn is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. Our goal is to foster an inclusive and accessible workplace where everyone has the opportunity to be successful. If you need a reasonable accommodation to search for a job opening, apply for a position, or participate in the interview process, connect with us at accommodations@linkedin.com and describe the specific accommodation requested for a disability-related limitation. Reasonable accommodations are modifications or adjustments to the application or hiring process that would enable you to fully participate in that process. Examples of reasonable accommodations include but are not limited to:

• Documents in alternate formats or read aloud to you
• Having interviews in an accessible location
• Being accompanied by a service dog
• Having a sign language interpreter present for the interview

A request for an accommodation will be responded to within three business days. However, non-disability related requests, such as following up on an application, will not receive a response. LinkedIn will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by LinkedIn, or (c) consistent with LinkedIn's legal duty to furnish information. Pay Transparency Policy Statement As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: Global Data Privacy Notice for Job Candidates This document provides transparency around the way in which LinkedIn handles personal data of employees and job applicants:

Job Tags

For contractors, Work experience placement, Flexible hours,

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