Network Engineer for Autonomous Systems Data Annotation

Turn hands-on L3 network troubleshooting and incident-response expertise into structured data that trains AI-driven networking systems. This role focuses on reviewing production network telemetry and artifacts, labeling and annotating behaviors and anomalies, and helping define schemas for large-scale data pipelines that will feed machine learning models.

• Inspect real-world network artifacts, including logs, configurations, telemetry streams, and event traces.
• Label and classify network behaviors, issues, anomalies, and incident patterns for downstream ML use.
• Perform packet analysis, deep troubleshooting, and root cause analysis style workflows to interpret incidents and produce consistent annotations.
• Contribute to the design and definition of data schemas and structures suitable for large-scale pipelines and model training.
• Collaborate directly with the client team to ensure labels and schemas meet engineering and ML needs.

• Current experience as a Level 3, Tier 3, or Principal Support Engineer handling final escalations, on-call rotations, RCAs, and outage troubleshooting, must have.
• Hands-on experience supporting end-customer enterprise networks, including switches, access points, and firewalls across environments such as retail, healthcare, finance, manufacturing, higher education, or hospitality, must have.
• Practical Wi-Fi and wireless skills, including enterprise WLAN controllers such as Cisco WLC, Aruba, or Meraki, 802.1X and RADIUS, and wireless troubleshooting experience, must have.
• Familiarity or curiosity about how infrastructure telemetry and raw data are transformed into inputs for machine learning models.

• Location: Remote.
• Employment type: hourly.
• Expected time commitment: 30 to 40 hours per week.
• You will interface directly with the client team during the engagement.

• Pay range: 50 to 70 hourly.

No additional work-authorization or visa sponsorship details were provided in the source materials. Applicants should ensure they can work under an hourly engagement in their current location.