Network Specialist - Fully Remote

mercor; San Francisco (CA); Hybrid; CAD 98,000 - 137,000; Part time

Job summary

Mercor is seeking a Network Engineer for Data annotation in an autonomous systems project. The role is remote on a contract basis, offering hourly compensation and a 30–40 hour weekly commitment.

You will review network data, label behaviors and anomalies, and help structure data pipelines to support AI model improvement. Ideal candidates have experience with enterprise networks and strong skills in interpreting logs and time-series metrics, with an interest in how raw infrastructure data

Qualifications

• Experience working as a network engineer, ideally with enterprise networks (switches, APs, firewalls).
• Comfort with interpreting logs, events, and time-series metrics.
• Curiosity about how raw infrastructure data becomes machine learning input.

Responsibilities

• Review real-world data from deployed networks, including logs, configs, telemetry, and event streams.
• Label and classify key behaviors, issues, and anomalies in network data.
• Help define schemas and structure for large-scale data pipelines.
• Interface directly with the client team to ensure data quality and relevance.
• Work independently and asynchronously to meet deadlines and improve AI model performance.

Skills

Network engineering

Log interpretation

Time-series metrics

Data labeling

Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: Network Engineer - Data for Autonomous Systems annotation

Type: Contract

Compensation: $50–$70/hour

Location: Remote

Commitment: 30–40 hours/week

Role Responsibilities

• Review real-world data from deployed networks, including logs, configs, telemetry, and event streams.
• Label and classify key behaviors, issues, and anomalies in network data.
• Help define schemas and structure for large-scale data pipelines.
• Interface directly with the client team to ensure data quality and relevance.
• Work independently and asynchronously to meet deadlines and improve AI model performance.

Qualifications

• Experience working as a network engineer, ideally with enterprise networks (switches, APs, firewalls, etc.).
• Comfort with interpreting logs, events, and time-series metrics.
• Curiosity about how raw infrastructure data becomes machine learning input.