Description review
Fazeshift | Senior Software Engineer | San Francisco, CA | Onsite 5 days | Full-time | $200k–$235k + equity
Fazeshift · San Francisco, CA · back to the listing
HR standards
63/100
needs work
Title ↔ description
87/100
strong
Reads as
Full-Stack Engineer
99% confident
How others title the same work
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What the listing never says
- No section describes what the person would actually do. Scope clarity
- No pay range published. Candidates cannot tell whether applying is worth their time. Pay transparency
- No location or timezone policy stated, so a candidate cannot tell where they may work from. Scope clarity
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.
I’m Chris, VP Engineering at Fazeshift. We build AI agents that do accounts-receivable work: billing, collections, payments, and cash application. We're nine engineers plus our Founder/CTO and me, and I'm hiring someone who can help us do more, better: increase both our production capacity and the quality and sophistication of the software we build.
We think AI changes what’s possible here. Instead of giving finance teams another dashboard to stare at, we’re building agents that actually do the work across billing, collections, payments, cash application, and the workflows connecting those systems.
The interesting engineering constraint: we’re intentionally not the system of record. We act across customers’ ERPs, banks, and billing systems. A situation where a timeout doesn’t necessarily mean a write failed, and retrying blindly can be the wrong answer.
A few of the questions we get to work on:
* How do you let agents take real actions safely? Permissions, auditability, retries, idempotency, durable execution, and knowing when a human needs to stay in the loop.
* How do you build a coherent product when you’re intentionally not the system of record? APIs are inconsistent, webhooks arrive late or twice, and a timeout doesn’t necessarily mean a write failed.
* How should an engineering team work when coding agents can take on meaningful chunks of implementation, debugging, testing, and exploration?
We’re especially interested in engineers who have already changed how they work because of AI. If you delegate meaningful work to agents, keep independent tasks moving in parallel, debug where they get stuck, and shave the occasional yak to get things working properly, you’ll feel at home here. We’re still figuring this out too (and updating our priors regularly).
At the same time, generated code is still your code. We care about judgment, debugging, systems thinking, taste, and understanding what you’re shipping.
Our approach is roughly: design enough to find the shape of the problem, then build to learn. There’s a short distance here between noticing a problem, building the fix, and seeing the result.
We’re looking for 5+ years of professional experience, strong TypeScript/full-stack skills, and production ownership. No fintech background needed.
Stack: TypeScript/Next.js, tRPC, Prisma/MySQL/PlanetScale, Inngest, AWS.
I’m Chris, VP Engineering, and I read these applications myself. Email [email protected] with HN in the subject, your LinkedIn/GitHub/résumé, and a few sentences about something you’ve built and how you knew it worked or how AI has changed your approach. No public code needed; please leave out confidential details.
We think AI changes what’s possible here. Instead of giving finance teams another dashboard to stare at, we’re building agents that actually do the work across billing, collections, payments, cash application, and the workflows connecting those systems.
The interesting engineering constraint: we’re intentionally not the system of record. We act across customers’ ERPs, banks, and billing systems. A situation where a timeout doesn’t necessarily mean a write failed, and retrying blindly can be the wrong answer.
A few of the questions we get to work on:
* How do you let agents take real actions safely? Permissions, auditability, retries, idempotency, durable execution, and knowing when a human needs to stay in the loop.
* How do you build a coherent product when you’re intentionally not the system of record? APIs are inconsistent, webhooks arrive late or twice, and a timeout doesn’t necessarily mean a write failed.
* How should an engineering team work when coding agents can take on meaningful chunks of implementation, debugging, testing, and exploration?
We’re especially interested in engineers who have already changed how they work because of AI. If you delegate meaningful work to agents, keep independent tasks moving in parallel, debug where they get stuck, and shave the occasional yak to get things working properly, you’ll feel at home here. We’re still figuring this out too (and updating our priors regularly).
At the same time, generated code is still your code. We care about judgment, debugging, systems thinking, taste, and understanding what you’re shipping.
Our approach is roughly: design enough to find the shape of the problem, then build to learn. There’s a short distance here between noticing a problem, building the fix, and seeing the result.
We’re looking for 5+ years of professional experience, strong TypeScript/full-stack skills, and production ownership. No fintech background needed.
Stack: TypeScript/Next.js, tRPC, Prisma/MySQL/PlanetScale, Inngest, AWS.
I’m Chris, VP Engineering, and I read these applications myself. Email [email protected] with HN in the subject, your LinkedIn/GitHub/résumé, and a few sentences about something you’ve built and how you knew it worked or how AI has changed your approach. No public code needed; please leave out confidential details.