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Meter

Network Engineer (with Data Focus) — Labeling & Structuring Data for Autonomous Systems

Reposted 24 Days Ago
In-Office or Remote
Hiring Remotely in San Francisco, CA
50-100
Mid level
In-Office or Remote
Hiring Remotely in San Francisco, CA
50-100
Mid level
Network Engineers needed to label and structure data for autonomous systems by reviewing real-world data and defining schemas for data pipelines.
The summary above was generated by AI
Contract Role: Network Engineer (with Data Focus) — Labeling & Structuring Data for Autonomous Systems

Type: 3 to 6 month contract
Location: Onsite Bay Area
Compensation: $50-$100 per hour (based on experience)

Are you a network engineer who’s curious about data science and wants to work at the intersection of infrastructure and intelligence?

At Meter, we’re building vertically integrated networking systems and now we’re using the data they generate to power the next generation of autonomous infrastructure. We’re looking for network engineers to help us label, annotate, and structure the data flowing through our systems.

This is a hands-on role that blends your knowledge of networks 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 key behaviors, issues, and anomalies
  • Help define schemas and structure for large-scale data pipelines

You’re a strong fit if you:

  • Must have experience working as a network engineer, ideally with enterprise networks (switches, APs, firewalls, etc.)
  • Comfortable interpreting logs, events, and time-series metrics
  • Curious about how raw infra data becomes machine learning input
  • Want to contribute to the future of autonomous networking systems

Your work will directly feed into the pipelines that power Meter’s AI models, and help shape how intelligent systems reason about networks in the real world.


Top Skills

AI
Data Pipelines
Machine Learning
Networking

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