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# Staff Infrastructure Engineer at Descript - $292K

- **Company:** Descript
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $220,000.0 - $292,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Distributed Systems, Key Management, Runbook, Delivery Pipeline, Kubernetes, Machine Learning Operations
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25304_bbcfabaac84c8eee30238f1f9f066328

## About the Role

* 8+ years building and operating production distributed systems, or equivalent server-side engineering with a heavy infrastructure focus.
 * You effectively leverage agents to multiply your impact and think critically about how and when to harness AI in your work.
 * You've run systems where failure was expensive, and your opinions about reliability and deployment come from consequences rather than reading.
 * You've carried a pager, commanded an incident, and rolled back before you understood why.
 * You use SLOs and error budgets as operating tools.
 * You've used a major cloud provider and Kubernetes in production, with infrastructure-as-code as your default.
 * You've owned an architecture or migration, from planning through launch, whose consequences outlived the project, and you can say what you'd do differently.
 * You've found important unowned work, scoped it, earned buy-in, and delivered it without being handed a spec.
 * You build a minimal repro, read the logs, and write targeted checks to prove a fix works instead of trusting output., * GPU and ML infrastructure: Capacity planning, training or inference pipelines, serving cost and latency. If you haven't run a GPU fleet, experience with expensive capacity-constrained systems transfers well.

## Description

* The AI enablement substrate: GPU capacity, training and inference pipelines, and the reliability and cost of the systems serving models in production.
 * Cost is an engineering constraint: Infrastructure decisions have a number attached and you're expected to make smart trade-offs. As inference grows with usage, the metering and attribution behind those numbers sit with this team.
 * Security comes with the systems you run: Identity and access, secrets management, least-privilege boundaries, and supply-chain integrity.
 * Make what you build legible: Infrastructure-as-code that explains itself. Runbooks and in-repo context written for someone jumping in to help. Observability that tells you what failed and why.
 * Improve how the team learns and ships: Form hypotheses, instrument your work, release incrementally, and read results honestly. Strengthen the tooling, standards, tests, observability, and release practices that help the team move quickly without compromising quality.
 * Raise the team's technical ambition: Provide architectural direction, thoughtful reviews, mentoring, and clear human writing.