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
What We Do
Meter is a developing next-generation volumetric imaging technology. We are building a machine that can see inside of anything and cloud-based software for processing the complex, volumetric data that the machine produces. Our technology will give engineers, designers, and eventually medical practitioners more confidence in their processes and lower the barrier to high quality imaging and inspection tools.
Our team of engineers includes world-class researchers, industrial designers, PhDs, founders of successful startups, and zero egos. We are backed by some of the top venture capital funds and angel investors in Silicon Valley and beyond. The company is headquartered in Cambridge, MA and has an office in San Francisco, CA.
