Mac Automation & Analytics Engineer, Leo Mac

Amazon Amazon · Big Tech · Redmond, WA · Software Development

Engineer to build and own the MAC Analyzer and MAC feature automation framework, leveraging ML-based anomaly detection and predictive capabilities for diagnostics and automated validation of MAC features.

What you'd actually do

  1. Design and develop the MAC Analyzer tool with ML-based anomaly detection and predictive capabilities, encoding experienced MAC engineer diagnostic methodologies into automated workflows that interface with SDN and telemetry databases
  2. Build self-improving analytical pipelines that automate triage, root cause analysis, and reporting from raw telemetry data, trained on historical contact data and engineer patterns
  3. Create intuitive interfaces that make MAC diagnostics accessible to non-domain experts, continuously expanding coverage based on new failure modes and field observations
  4. Design, develop, and maintain an automated test framework covering MAC features (QoS, scheduling, grant handling, multicast, traffic shaping) integrated into the payload test framework and release qualification loop
  5. Automate scenario-based testing for complex MAC configurations (simplex DL/UL combinations, MCS edge cases, burst handling) with traffic generation tools that replicate field conditions in lab environments

Skills

Required

  • software development
  • data analysis
  • test automation
  • database querying
  • algorithmic workflows
  • test frameworks
  • automation pipelines
  • machine learning frameworks
  • data processing at scale
  • tools or platforms used by cross-functional teams
  • communication skills

Nice to have

  • wireless/MAC layer systems
  • telecommunications
  • telemetry data pipelines
  • observability tooling
  • statistical analysis
  • anomaly detection
  • time-series forecasting
  • release qualification processes
  • CI/CD integration

What the JD emphasized

  • ML-based anomaly detection
  • predictive capabilities
  • automated validation
  • automated test framework
  • telemetry data

Other signals

  • ML-based anomaly detection
  • predictive capabilities
  • self-improving analytical pipelines
  • automated triage
  • root cause analysis