Sr. Software Engineer, AI Infrastructure at LinkedIn
Sunnyvale, CA
$139,000–$229,000from the description
Jul 15, 2026
Sunnyvale, CA
Jul 21, 2026
What this job asks for AI summary
A senior engineering role focused on building and optimizing large-scale AI infrastructure, spanning both model training and model serving. Day-to-day work involves distributed training of very large models, low-latency GPU inference, performance tuning at the framework and hardware level, and contributing to open-source deep learning projects. The role suits engineers with hands-on experience in deep learning systems, distributed computing, and ML frameworks who are comfortable taking technical ownership across complex, cross-team initiatives.
Senior level · 2+ years · San Francisco-Oakland-Berkeley, CA · Bachelor's required · Full-time
“or” means any one of them counts — you don't need all of them.
Posted 3 times — it's one opening, so apply once.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Roughly 430 people in the San Francisco-Oakland-Berkeley, CA area plausibly meet what this posting asks for (software developers). range 320–560
Applicant volume Moderate — A normal amount of company. The rare requirements below are what will separate a shortlisted application from the rest.
Rare in this occupation — lead with these, and say what you built with them.
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $190,744 (middle half $167,095–$224,501). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.
Why we read it this way (7)
The compensation range ($139,000–$229,000/year) explicitly includes potential non-discretionary annual performance bonus and/or other incentive compensation, plus possible stock grants — the range is not base salary alone.
The role is hybrid (home + LinkedIn office), not fully remote. The posting references San Francisco Fair Chance Ordinance, indicating a Bay Area office location.
The Basic Qualifications gate on 2+ years of industry experience with deep learning systems AND 2+ years with at least one of a broad list of languages (Java, C++, Python, Go, Rust, C#, Scala, or other functional languages). Python is listed as the primary skill name with the others captured as alternatives.
The Preferred Qualifications section offers a tiered experience ladder (BS+5, MS+4, PhD+2 years). These are preferred, not minimum requirements — the hard minimum remains 2+ years from the Basic Qualifications.
Deep learning frameworks (PyTorch, TensorFlow, JAX/FLAX), ML infrastructure tools (MLflow, Kubeflow), distributed data engines (Flink, Beam, Spark), containers/orchestration, and open-source project experience all appear under Preferred Qualifications or the 'Suggested Skills' section and are marked accordingly.
The 'Suggested Skills' block (ML Algorithm Development, Information Retrieval/Recommendation Systems, Big Data) names no specific tools and so is omitted from the skills list per extraction rules.
The degree requirement is a Bachelor's in CS or related field; however, the JD also accepts 'equivalent practical experience,' which would normally yield the degree requirement of None. The Basic Qualifications phrasing ('or equivalent practical experience') is noted here — the degree requirement is set to Bachelors as the stated preference, but equivalent experience is explicitly accepted.
Read the full posting
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