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# Software Engineer, Models

- **Company:** Meter, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Starter
- **Contract:** Permanent contract
- **Skills:** Training Data, Encodings, Data Governance, Data Pipelines
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25197_cf2bfb4b300bcf399cc86619b93b1862

## About the Role

* Can prototype quickly and have the skill and fortitude to bring it to production when the time is right
 * Build for the end user: you care whether the people using your tools actually enjoy using them
 * Are curious about domains outside engineering - you'll need to learn how network engineers think, not just what they do
 * Communicate clearly with both engineers and domain experts
 * Thrive with high autonomy and are energized by defining the work, not just executing it, * Experience with data labeling, active learning, or annotation frameworks
 * Understanding of real-time telemetry and large-scale time-series data
 * Background building internal tools or developer-facing products

## Description

Nobody is building what we're building, and a big reason for that is data. If we can systematically capture how great network engineers think and encode it into our models, we build something genuinely unreplicable.

That's what this role is about. You'll own the systems and tooling that turn Network Engineer expertise into high-quality training data for our foundation models. This is a 0-1 role: you'll spend real time alongside Network Engineers learning how they think before you write a line of code, then build fast, iterate faster, and scale what works.

You'll know you've succeeded when the Network Engineering team can generate training data independently, at volume, and model performance moves as a result., * Design, implement, and iterate on the tools that network engineers use to annotate issues, log diagnostic reasoning, and curate data for model training and evaluation

 * Build interfaces that feel natural to domain experts: low friction, high signal, and easy to use without engineering support
 * Partner closely with network engineers to continuously learn what's working and what isn't, and update the tooling accordingly, * Set up metrics and feedback loops that measure tooling effectiveness and its downstream impact on model quality
 * Monitor and troubleshoot data pipelines and tooling in production
 * Help define best practices for labeling workflows and data governance