Intel
Principal Engineer
$234K–$383K
US, Oregon, Hillsboro +3 · Principal · Full-time · Posted Oct 27, 2026
- Location
- US, Oregon, Hillsboro
US, California, Folsom
US, California, Santa Clara
US, Arizona, Phoenix - Experience
- Bachelor’s: 12+ years; Master’s: 8+ years; PhD: 6+ years. Additionally, 7+ years building field telemetry and data collection systems at scale.
- Education
- Bachelor’s in Computer Science, Software Engineering, or related field; or Master’s; or PhD.
About the job
Leads architecture and development of Intel’s end-to-end telemetry and crash analytics platform to improve software reliability and product quality at scale.
What you'll do
- Own telemetry architecture from data collection through analytics and insights delivery, and scale ingestion across millions of endpoints.
- Drive crash analytics, reliability modeling, and predictive quality metrics to accelerate root-cause analysis and reduce failures.
- Set engineering standards, mentor teams, and partner across product, validation, quality, OEM, and other teams to turn telemetry into business actions.
What they ask for
- Deep knowledge of Intel client platform architecture and Windows and Linux OS internals, including crash and reliability paths.
- Software architecture expertise for scalable, secure, resilient high-volume data pipelines; hands-on experience with data lakes and analytics stacks.
- Applied analytics or machine learning experience in failure classification, de-duplication, anomaly detection, or predictive quality trending.
- Bachelor’s in Computer Science, Software Engineering, or related field with 12+ years of experience; or Master’s with 8+; or PhD with 6+. Also requires 7+ years building large-scale field telemetry and data collection systems.
Nice to have
- Experience partnering with OEMs, Microsoft, and third-party telemetry providers on data-sharing partnerships.
- Experience presenting at industry conferences and participating in standards committees.
Pay
$233,990.00-383,460.00 USD (annual salary range)
Tools
Windows, Linux, Snowflake, Databricks
AI focus
Applies analytics and machine learning to failure classification, bucketing, de-duplication, anomaly detection, and predictive quality trending.
Model-written summary · the original posting is authoritative