Sr. Staff Software Engineer, Systems Infrastructure
Mountain View, CA, United States · Staff+ · Contract
Sponsorship not specified$198k-$326kDetected 108 days ago
PythonJavaGoC++Distributed SystemsFull-Stack DevelopmentMachine LearningDeep LearningTensorFlowPyTorchLLMsMLOpsLeadership
Stay score
odds of building a lasting career here
61Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →Employer immigration record
from this employer's Department of Labor filings
Green-card filing pattern in this occupation
Context, not a finding about this posting: of this employer's 110 green-card filings in this occupation, 98% were for a worker who already held the job.
Green-card follow-through: 88%
Of 120 labor certifications old enough to have been used, 15 expired without the employer filing the next step. Median time from filing to decision: 493 days.99% of their filings were for a worker who already held the job.Only certifications past the 180-day window are counted — recent ones cannot have expired yet.
Reported a layoff in this occupation
In a green-card filing decided 2026-06-17, this employer stated it had laid off workers in Software Developers (CA) during the preceding six months. 3 such filings on record.Describes the period before that filing, not necessarily today. Unlike WARN notices this is occupation-specific and federal.
Files H-1B transfers
233 transfer filings in the last year, covering 1907 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
Community outcomes
No reports yet — be the first to help the next applicant.
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.
Responsibilities
- 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
- Please use this link to access documents that provide information about how LinkedIn handles the personal data of employees and job applicants, as well as the E-Verify Participation Notice and the Department of Justice Immigrant and Employee Rights Section Right to Work posters: https://www.linkedin.com/legal/candidate-portal.
Requirements
- 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
Nice to have
- 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
Skills
- 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.
Compensation
- As a federal contractor, LinkedIn follows the Pay Transparency and non-discrimination provisions described at this link: https://lnkd.in/paytransparency.
Company info
- We are looking for a Senior Staff Software Engineer with deep expertise at the intersection of systems, machine learning, GPU infrastructure, and large-scale inference.
- Equal Opportunity Statement We seek candidates with a wide range of perspectives and backgrounds and we are proud to be an equal opportunity employer.
Equal opportunity
- 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.
Visa & Work Authorization
- in, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class
Apply directly at LinkedIn →Create a free account for alerts like thisView LinkedIn immigration profile
This listing is sourced directly from LinkedIn's careers page and normalized into a canonical job model.