Datum

DX / AX & Forward Engineering Lead

Remote (US)

Sponsorship not specifiedDetected 29 days ago

KubernetesLLMsLogisticsLeadership

About the role

Datum's most important core value https://www.datum.net/handbook/about/purpose/ is to be connectors: of people, businesses, apps, and networks.
That's why we're on a mission to help the next 1k thrive in the AI era with an ecosystem-led network cloud.
But increasingly, the user of that network isn't a person.

Own the design philosophy for how agents discover, understand, and get value from Datum, all without reading a marketing site, without a human in the loop, and without surprises
Drive our investment in CLIs, declarative configs, and APIs as the primary surface area, more than ever
Own the developer experience end-to-end, from the browser to the command line
Partner to keep documentation accurate, opinionated, and treated as a first-class deliverable (for humans and for the models that will read it)
Build out a small forward-deployed engineering function that works directly with AI-native providers, alt clouds https://www.alt-cloud.org, and partners on real production workloads
Drive your roadmap by modeling Datum's values every day
You have the taste to know what to build and ship

You've used a lot of CLIs, SDKs, or declarative APIs and you know what people (and now agents) actually love.
You know, love and hate Kubernetes as much as we do.
Open source track record.
Logistics: Overlap with NYC business hours and ability to travel to NYC, SF, etc
Rock-solid APIs, squeaky-clean docs, and a developer experience that holds up at AI-native scale

of applications, services, networks, and people.
It's more secure, more operable, and even more enjoyable.
Still interested?
Push the platform toward deterministic, composable, well-typed primitives from first touch to final invoice
Make "time to first successful tool call" a metric that the whole team is proud of
Maximize dopamine, minimize friction

This listing is sourced directly from Datum's careers page and normalized into a canonical job model.