Role overview
Reporting to the Director of Automation, Tooling, and Observability within Global Network Engineering & Operations (GNEO), the Machine Learning / Software Engineer plays a critical role in designing, developing, and implementing self-healing infrastructure management systems for enterprise-wide, production environments. This role combines deep expertise in machine learning, AI technology, software engineering, and DevOps to create reusable patterns, frameworks, and services to improve reliability across Services and Platforms. The candidate will serve as a thought leader, identifying opportunities for and applying advanced analytics, predictive modeling, and AI to large-scale telemetry, changes, events and incident data to derive actionable insights. The role focuses on building, deploying, and operating machine learning models that proactively detect issues, predict failures, and drive automated, self-healing remediation across enterprise systems. The role is intentionally machine learning and AI heavy and is intended to be a strategic driver in that space.
What we're looking for
- Master’s degree in Computer Science, Engineering, or related discipline.
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The hiring range for this position in Burbank, CA is $155,700 - $208,700 per year and in Seattle is $163,100 - $218,700 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.