Network Engineer - Data for Autonomous Systems annotation | $50-$70/hr Remote
Overview
This role is a unique blend of network engineering and data annotation for AI systems. You'll apply your deep L3 troubleshooting experience to label and structure real-world network data, directly feeding the machine learning pipelines that power autonomous infrastructure. It's a chance to move beyond keeping networks running and into shaping how intelligent systems understand them.
What You'll Do4
- 1Examine live data from production networks—logs, telemetry, event streams—and identify meaningful patterns, anomalies, and incident signatures.
- 2Annotate and categorize network behaviors, issues, and root causes to create clean, structured datasets for downstream ML models.
- 3Collaborate with the client team to define schemas and data formats that make raw infrastructure data usable for AI training.
- 4Work 30-40 hours per week on a flexible remote schedule, directly influencing how autonomous systems reason about real-world networks.
Requirements3
- 1Must have proven experience as a Level 3 / Tier 3 or Principal Support Engineer, handling final escalations, post-mortems (RCAs), and complex outage troubleshooting in enterprise environments.
- 2Hands‑on familiarity with enterprise network hardware such as switches, access points, and firewalls in sectors like retail, healthcare, finance, or manufacturing.
- 3A strong curiosity about how raw operational data becomes input for machine learning and a desire to help build the future of autonomous networking.
Who Should Apply
The ideal candidate is a seasoned network troubleshooter who is tired of putting out fires and wants to help teach machines how to prevent them. You combine deep L3 expertise with a genuine interest in data science, enjoy documenting and classifying problems, and are comfortable working independently on a focused annotation project.
Salary Insight
Compensation is $50.00 – $70.00 per hour, paid on an hourly basis for this remote contract role.
Application Tip
When applying, include a one‑page summary that maps a specific network incident you resolved (with RCA) to how you would structure that data for an ML pipeline. This shows you understand both the engineering and the annotation mindset.
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