Backend Engineer, Data Centers

The internet runs the world–data centers, offices, manufacturing facilities, schools, etc., spend billions annually on computer networking to stay productive.

However, these networks are messy and brittle. Traditional networking companies only build one piece of the puzzle—either hardware, software, or operations—but never all three, leading to inefficient silos and slow changes. Network engineers—the lifeblood of the industry—are often left scrambling to fix issues, hand editing configs, guess-and-checking solutions, and worrying how the ever-increasing complexity of the system they manage will break.

Meter has spent the last decade vertically integrating the fragmented computer networking industry across hardware (power, routing, switches, security, wireless), firmware, operating systems, and real-world operations. By owning the full stack, we make it exceptionally easy for any business to have great networking, internet, and Wi-Fi. We believe people who build hardware and software should be the ones responsible for it. Our ambition is to evolve internet infrastructure into a utility, and we recently raised a $170M Series C to advance our work.

We've extended Meter's hardware, firmware, and infrastructure platforms to include Ethernet top-of-rack and spine switching.

Paired with Meter's end-to-end delivery model, customers get one accountable operator instead of a stitched-together stack of vendors — the team that designs the hardware also writes the OS, builds the monitoring software, and runs the deployment. Pricing is tied to power consumption, which is how businesses actually buy data center capacity today. Learn more about our approach to automation at meter.ai.

Meter has ambitious growth goals as we build switching products for data center users.

These networks have significant feature, reliability, performance, and monitoring requirements. We’ll also be heavily scrutinized as a new vendor in this market. Your job is to work closely with our firmware and frontend teams to ensure our infrastructure scales to meet these requirements.

• Working in Go and Python, ensure backwards compatibility and feature versioning across constantly evolving schemas

• Build scalable pipelines for ingesting, transforming, and serving petabytes of data across Kafka, Postgres, and Clickhouse

• Create CI/CD workflows that evolve the API in lockstep with changes to the underlying data schema

• Enable fine-grained querying of historical and real-time data for any network, at any point in time

• Help define and enforce the principle of "smart data, dumb functions": doing as much as possible in the data layer to keep downstream code minimal

• Collaborate with modelers to co-design training and evaluation pipelines that are reproducible, debuggable, and fast

• Own performance across key endpoints to meet real-time serving constraints