Senior Staff Software Engineer, AI Infrastructure at LinkedIn
Sunnyvale, CA
$198,000–$326,000from the description
Aug 17, 2026
Sunnyvale, CA
Sep 25, 2026
What this job asks for AI summary
A Sr. Staff Software Engineer role on LinkedIn's Model Evaluation and Model Observability teams, responsible for defining technical strategy and building large-scale infrastructure that enables ML engineers and researchers to measure, monitor, and diagnose AI model quality in both offline and production environments. The work spans distributed systems, real-time telemetry ingestion, model drift detection, and evaluation frameworks covering recommendations, search, ads, and generative AI. Suited to a senior technical leader with deep ML infrastructure and distributed systems experience.
Staff level · 5+ years · San Francisco-Oakland-Berkeley, CA · Full-time
“or” means any one of them counts — you don't need all of them.
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 370 people in the San Francisco-Oakland-Berkeley, CA area plausibly meet what this posting asks for (software developers). range 200–480
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 Aug 20, 2026. It is a model, not a headcount.
Why we read it this way (7)
The role is hybrid (not fully remote), based at a LinkedIn office — the posting references San Francisco Bay Area cost-of-labor context and LinkedIn's headquarters in Sunnyvale/Mountain View, CA; the CBSA is assigned as San Francisco-Oakland-Berkeley, CA, the closest major CBSA.
The basic qualifications list '5+ years' as the floor for several dimensions (software design, programming, infrastructure/ML/distributed systems) and '2+ years' in a technical leadership position; the overall years minimum is set to 5 as the stated overall floor.
Programming language requirement lists Python, C++, Java, Go, Rust, and Scala as interchangeable options — one primary language from this set is required; alternatives are captured accordingly.
A BS/BA in Computer Science or equivalent experience is listed as a basic qualification, but the 'or equivalent technical experience' clause means no formal degree is hard-gated.
Preferred qualifications include MS or PhD, 10+ years of experience, and specific ML infrastructure/observability/generative-AI experience — all marked as preferred.
OpenTelemetry (OTEL) and OpenInferenceTelemetry are named in the preferred section as examples of telemetry standards; captured as a single preferred skill with the alternative noted.
The 'Suggested Skills' block (Model Evaluation, ML Observability, ML Infrastructure, Production ML Systems, Large-Scale Distributed Systems, MLOps) appears to be LinkedIn's own skill-tagging feature rather than a separate requirements section; skills from it that are not already captured from the body are included as preferred.
Read the full posting
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