Sr. Staff Software Engineer - HPC Network Engineering at LinkedIn

Mountain View, California, United States -

Full Time

Start Date

Immediate

Expiry Date

17 Jun, 26

Salary

0.0

Posted On

19 Mar, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

HPC Networking, RoCE v2, Ethernet Fabrics, GPU Clusters, AI/ML Training, RDMA, Lossless Ethernet, Clos/Fat-tree/Leaf-Spine, 400G/800G Switching, NIC Validation, Host Networking, Kubernetes Pod Networking, Performance Tuning, Distributed Systems, Technical Leadership, System Design

Industry

Software Development

Description

Company Description LinkedIn is the world’s largest professional network, built to create economic opportunity for every member of the global workforce. Our products help people make powerful connections, discover exciting opportunities, build necessary skills, and gain valuable insights every day. We’re also committed to providing transformational opportunities for our own employees by investing in their growth. We aspire to create a culture that’s built on trust, care, inclusion, and fun – where everyone can succeed. Join us to transform the way the world works. Job Description 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. This role will be based in Mountain View, CA. We are seeking an HPC Network Engineer to design, deploy, and operate high-performance, low-latency Ethernet fabrics for large-scale GPU clusters. The role focuses on RoCE v2–based GPU interconnect networks supporting AI/ML training, inference, and HPC workloads. You will work closely with systems, GPU, platform, and software teams to build scalable, lossless Ethernet networks optimized for RDMA traffic. As a Senior Staff Software Engineer, you will define long-term technical direction, lead cross-org initiatives, mentor senior engineers, and drive solutions for complex distributed systems challenges at massive scale. This role requires deep expertise in backend systems, data processing, and large-scale system design, with strong understanding of networking concepts. Responsibilities: Network architecture and design for large-scale LLM training and inference workloads. Design RoCE v2–based GPU interconnection fabrics for multi-rack and multi-pod GPU clusters Define lossless Ethernet architectures (Clos / fat-tree / leaf-spine) optimized for RDMA Select and validate 400G / 800G Ethernet switching platforms and NICs (ConnectX, BlueField, etc.) Deep expertise in host-level and Kubernetes pod networking architectures, including enablement of high-performance features such as RDMA and GPU Direct. Experience in host network performance tuning for large-scale collective communications, balancing latency, throughput, and congestion control. Analyze system performance and diagnose complex cross-layer issues. Qualifications Basic Qualifications: BA/BS Degree in Computer Science or related technical discipline, or equivalent practical experience 10+ years of experience building and operating large-scale distributed systems or data-intensive backend platforms. Experience in one or more programming languages such as Go, Python, C++, or similar. Experience in Linux system engineering and host networking. Demonstrated knowledge of network protocols, fabric design, and performance optimization. Proven ability to lead complex technical initiatives end-to-end in a multi-team environment. Experience with system design skills with focus on scalability, reliability, and performance. Experience with container platforms (Kubernetes) and microservices. Preferred Qualifications: Experience supporting large-scale AI or HPC workloads. Familiarity with LLM training frameworks and communication libraries (e.g., NCCL, MPI). Experience with streaming systems (Kafka, Flink, Spark Streaming, or similar) and high-throughput data pipeline architectures. Experience with performance benchmarking and profiling tools. Experience with infrastructure automation or configuration management tools. Demonstrated influence across organizations (tech lead, architect, principal/IC leadership roles). Suggested Skills: Distributed Systems HPC Networking Performance Optimization Technical Leadership You will Benefit from our Culture We strongly believe in the well-being of our employees and their families. That is why we offer generous health and wellness programs and time away for employees of all levels. LinkedIn is committed to fair and equitable compensation practices. The pay range for this role is $181,000 to $297,000. 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.linkedin.com/benefits. Additional Information 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 [email protected] 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. San Francisco Fair Chance Ordinance Pursuant to the San Francisco Fair Chance Ordinance, LinkedIn will consider for employment qualified applicants with arrest and conviction records. Pay Transparency Policy Statement As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency. Global Data Privacy Notice 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.linkedin.com/candidate-portal. Workplace Type: Hybrid Career Track & Grade: IC5/10 Department: Engineering

Responsibilities

This role involves designing, deploying, and operating high-performance, low-latency Ethernet fabrics, specifically focusing on RoCE v2-based GPU interconnect networks for AI/ML and HPC workloads. The engineer will define long-term technical direction, lead cross-organizational initiatives, and mentor senior engineers to solve complex distributed systems challenges at scale.