Sr. Staff Software Engineer, Systems Infrastructure

Mountain View, CA, United States · Staff+ · Contract

Sponsorship not specified$198k-$326kDetected 22 days ago

PythonJavaGoC++Distributed SystemsFull-Stack DevelopmentMachine LearningDeep LearningTensorFlowPyTorchLLMsMLOpsLeadership

About the role

This role will be based in Sunnyvale or Mountain View, CA.
At LinkedIn, our approach to flexible work is centered on trust and optimized for culture, connection, clarity, and the evolving needs of our business.

Lead the design, development, and optimization of LinkedIn's large-scale LLM serving infrastructure
Drive performance improvements across AI inference systems, including latency, throughput, GPU utilization, and cost efficiency
Build and scale online and offline inference systems for LLMs and other AI models
Optimize model execution across the full stack, including model architecture, runtime, compiler, kernel, and hardware layers
Drive model optimization techniques such as quantization, pruning, compression, batching, and memory optimization
Partner closely with ML, infrastructure, and product teams to identify serving bottlenecks and improve end-to-end model performance
Set technical direction for model serving, inference performance, and next-generation AI infrastructure design
Documents in alternate formats or read aloud to you

BA/BS degree in Computer Science or related technical field, or equivalent practical experience
Experience with GPU-based systems, CUDA, kernel optimization, or hardware-aware performance tuning
Experience with large-scale inference systems, including latency, throughput, reliability, and cost optimization
Experience programming in one or more systems languages such as C++, Go, Python, or Java

Deep experience with LLM serving infrastructure, AI inference platforms, or large-scale model deployment systems
Familiarity with or contributions to open-source serving frameworks such as vLLM, SGLang, Triton, TensorRT, Ray, or similar technologies
Experience with ML compilers, runtimes, or graph optimization frameworks such as XLA, TVM, TensorRT, Triton, or similar
An understanding of model optimization techniques such as quantization, pruning, compression, batching, caching, and memory optimization
Experience improving GPU utilization and cost/performance efficiency for large-scale ML workloads
An understanding of distributed systems, scheduling, resource management, and large-scale infrastructure operations
Experience operating across the stack from model-level optimization to runtime, compiler, kernel, and hardware-level performance improvements
Experience influencing technical direction across teams and partnering effectively with ML researchers, infrastructure engineers, and product teams

Join us to transform the way the world works.
The work focuses on making large-scale models run faster, cheaper, and more efficiently on GPUs at LinkedIn scale.

Policy Statement
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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.

in, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class

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