Network Engineer - Data for Autonomous Systems annotation

Compensation: $50-$70 per hour

Are you a Level 3 / Tier 3 network support engineer interested in data science and autonomous infrastructure? Our client is building vertically integrated networking systems and using the data they generate to power the next generation of AI-driven infrastructure. They’re looking for engineers experienced in final escalations, packet analysis, troubleshooting, and RCA workflows to help label, annotate, and structure networking data from real production systems.

This is a hands-on role that blends your L3 troubleshooting and incident-response experience with a growing understanding of how data pipelines are built and used in AI systems.

In this role, you’ll

• Review real-world data from deployed networks: logs, configs, telemetry, event streams
• Label and classify network behaviors, issues, anomalies, and incident patterns
• Help define schemas and structure for large-scale data pipelines that downstream ML models will train on

Key Responsibilities

• Analyze and troubleshoot complex enterprise networking issues and infrastructure incidents.
• Support investigations related to network outages, service degradation, performance bottlenecks, and escalated customer issues.
• Participate in root cause analysis (RCA) reviews and document technical findings.
• Contribute domain expertise regarding enterprise networking environments and operational best practices.
• Review and interpret network telemetry, logs, alerts, and infrastructure data.
• Collaborate directly with the client team to provide technical insights that improve AI training datasets and model performance.
• Help bridge the gap between real-world network operations and machine learning applications.
• Provide feedback on networking scenarios, operational workflows, and troubleshooting methodologies used in enterprise environments.

Required Qualifications

• Currently working as a Level 3, Tier 3, Senior, Lead, or Principal Support Engineer supporting enterprise network infrastructure.
• Hands-on experience managing, troubleshooting, and supporting enterprise networking environments.
• Strong experience with technologies such as:
• Network switches
• Wireless access points (APs)
• Firewalls
• Routing and switching infrastructure
• Proven experience handling:
• Production incidents
• Final-level escalations
• On-call support responsibilities
• Root cause analysis (RCA)
• Network troubleshooting and outage resolution
• Experience supporting end-customer environments across industries such as:
• Retail
• Healthcare
• Financial Services
• Manufacturing
• Higher Education
• Hospitality
• Large Enterprise Organizations
• Strong analytical and problem-solving skills.
• Excellent written and verbal communication skills.

Preferred Qualifications

• Curiosity about machine learning, AI systems, and data-driven infrastructure operations.
• Interest in understanding how raw infrastructure and operational data is transformed into machine learning inputs.
• Experience working with network monitoring, observability, or operational analytics platforms.
• Familiarity with automation, network operations, or intelligent infrastructure technologies.

Work Arrangement

• Fully remote engagement.
• Flexible schedule with an expected commitment of approximately 30–40 hours per week .
• Direct collaboration with the client team.
• Projects may be extended, shortened, or concluded based on business needs and performance.

Contract & Payment Details

• Independent contractor engagement.
• Weekly payments via Stripe or Wise based on approved services rendered.
• Work performed will not involve access to confidential or proprietary information from any employer, client, or institution.

Equal Opportunity

Qualified applicants will be considered without regard to legally protected characteristics. Reasonable accommodations are available upon request.

Please Note: Sponsorship for H-1B visas or STEM OPT programs is not available for this engagement.

Where you can work

This role is 100% remote, for applicants located in Canada and United States.