LinkedIn Benchmark for Software Engineering Professionals
EstablishedSee how Software Engineering professionals compare across LinkedIn visibility, career strength, and AI resilience.
Updated October 5, 2026
Free · Uses your LinkedIn export · No scraping
As of October 5, 2026, among RankedIn users classified as Software Engineering professionals, the median LinkedIn Score is 53 (middle half 44–59; 109 users) and the median AI Resilience Score is 70 (middle half 60–78; 110 users).
Career is the highest-scoring dimension (median 80) and Content the lowest (median 23).
These figures describe RankedIn users who uploaded a LinkedIn data export, a self-selected group rather than a representative sample of the profession. AI Resilience Scores are AI-generated estimates, so small differences between professions should not be read as meaningful.
How RankedIn calculates benchmarks →What stands out
You've seen the Software Engineering benchmark. Now see where you stand.
See where you fall within this benchmark — see your LinkedIn Score, AI Resilience, and individual signals next to Software Engineers, including where you're strong, where you may be under-positioned, and what to improve next.
See Where I Stand →Built from your actual LinkedIn data — not a questionnaire. Processed privately and never shared.
How does the comparison work? →
- Request your LinkedIn data export (LinkedIn → Settings → Data Privacy → Get a copy of your data)
- Upload the relevant files to RankedIn
- RankedIn analyzes the data
- See your personalized comparison and recommendations
About this benchmark(sample size, methodology)
The deepest benchmark maturity — a large, stable sample. Benchmarks publish only after meeting minimum data-quality and privacy thresholds — small or unverified samples are never shown as if they were reliable.
- Based on 110 benchmark-eligible Software Engineering professionals
- Benchmark values use the median score across eligible profiles
- Typical benchmark range: 44–59
- Population: Working professionals
- Maturity: Established
- Last updated October 5, 2026 · refreshed weekly
- Methodology version v2
- Sample size increased from 109 to 110.