Description review

Mastra | Customer Engineer | REMOTE (AMER or EMEA time zones) | Full-time

Mastra · REMOTE (AMER or EMEA time zones) · back to the listing

HR standards

77/100

solid

Title ↔ description

75/100

solid

Reads as

Full-Stack Engineer

85% confident

How others title the same work

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What the listing never says

  • No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency

The listing, marked up

Nothing in the wording of this listing tripped a check. The scores above still judge how complete and coherent it is.

Mastra is the open-source TypeScript framework for building AI agents (agents, workflows, memory, evals, tracing). From the team behind Gatsby, YC-backed,https://github.com/mastra-ai/mastra
We're hiring a customer engineer. Some companies would call this an FDE; we don't, because roughly half the job is shipping in the framework itself. The other half you're embedded with paying customers deploying agents to production: in their Slack channels and on calls, writing code in their repos, reviewing their PRs, working through architecture with their engineers. What you learn there comes back as bug fixes, features, and new primitives in Mastra. One of our current customer engineers is among the top bug-fixers in the entire codebase.
You should be a full-stack TypeScript engineer who has built agents and who can explain technical decisions clearly to engineers who haven't seen your code.
Remote. We have a lot of EU customers and response-time SLAs, so US Eastern/Central or EU hours work best.
Process: a call with Shane (co-founder/CPO, leads customer engineering), a conversation with a customer engineer, a 2-3 hour take-home, then you present it to us and get feedback.
Apply: {https://jobs.ashbyhq.com/Mastra/05172cb0-47ec-44f9-9fe8-07d4896d10ad} We get back to everyone.

How this was produced

Highlights are found by rule, not by a model: each one is a phrase matched at a known position, and every note is a template we wrote. The two scores come from a typed-decision model (Jev) that reads the listing against the official role definition and real listings for the same role, and returns probabilities rather than prose — it never writes any of the words on this page, and never chooses what to highlight.

Deterministic penalty applied to the HR score: 4 points (from 81 before penalties). Reviewed 21 Sep 2026.