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Job Overview
Location
Indiana, USA
Job Type
Full-time
Category
Software Engineering
Date Posted
October 8, 2025
Full Job Description
đź“‹ Description
- • Be the founding QA voice at Stack AI, the no-code platform that lets non-technical teams design, test, and deploy AI workflows powered by GPT-4 and other large language models. You will be the first dedicated quality hire in a fast-moving, YC-backed engineering org that already serves thousands of users daily.
- • Own the entire quality lifecycle—from exploratory testing of brand-new AI features to building bullet-proof automated suites that run in every CI/CD build. Your work will directly determine how confidently we ship updates that affect data pipelines, LLM prompts, vector stores, and customer-facing dashboards.
- • Design, document, and execute comprehensive test plans that cover web UI, REST/GraphQL APIs, backend Python services, and asynchronous Temporal workflows. You’ll translate complex user stories into clear acceptance criteria and edge-case matrices that the whole team trusts.
- • Build and extend automated test frameworks in TypeScript (Cypress or Playwright) and Python (PyTest). You’ll integrate these suites into GitHub Actions so that every pull request is validated in minutes, not hours, and release branches get a green check before they ever reach production.
- • Run regression, performance, and load tests using tools like k6 or Locust to ensure that new vector-search features or prompt-engineering upgrades don’t degrade latency or throughput. You’ll define SLIs/SLOs and set up alerts that page the right engineer before customers notice a hiccup.
- • Collaborate shoulder-to-shoulder with MIT & Stanford alums who are building the next generation of AI infrastructure. You’ll sit in on architecture reviews, debate trade-offs, and influence product decisions by quantifying risk and reliability trade-offs for every new feature.
- • Establish the QA culture playbook: bug-severity rubrics, release-sign-off templates, on-call runbooks, and post-mortem templates. Future QA hires will inherit your standards and tools, making you the architect of a quality program that scales with the company.
- • Debug gnarly issues that span browser, API, and model layers—think prompt drift causing a UI race condition—and reproduce them in deterministic test cases. You’ll pair with developers to isolate root causes and verify fixes under real-world load.
- • Champion observability by adding structured logs, metrics, and traces that make anomalies visible in Datadog or Grafana. When an alert fires, you’ll be the first to triage, ensuring that customer-facing SLAs stay above 99.9%.
- • Ship fast without sacrificing quality. In a startup where every sprint ends in a user-visible release, you’ll balance speed and rigor, using feature flags and canary deployments to test in production safely.
🎯 Requirements
- • 2–4 years of hands-on QA or test-automation experience with both web applications and APIs
- • Proficiency in Python and strong familiarity with TypeScript/JavaScript testing stacks (Cypress, Playwright, or Jest)
- • Proven ability to build and maintain automated test suites integrated into CI/CD pipelines (GitHub Actions, Render, or similar)
- • Deep understanding of REST/GraphQL testing, Git workflows, and modern release processes
- • Nice-to-have: experience validating AI/ML model outputs, data pipelines, or backend services built on Temporal, PostgreSQL, Redis, and Docker
🏖️ Benefits
- • Competitive salary and meaningful equity in a Y Combinator–backed startup
- • Work with a world-class, tight-knit team from MIT, Stanford, and top-tier research labs
- • Flexible remote-first culture with periodic offsites in San Francisco
- • Annual learning stipend for conferences, courses, and the latest AI/QA tooling
Skills & Technologies
Python
JavaScript
TypeScript
FastAPI
PostgreSQL
Senior
Onsite
Remote
About Stack AI Inc.
Stack AI Inc. provides a low-code platform for building, deploying and scaling AI workflows and applications. Its cloud service offers drag-and-drop builders, pre-built templates, vector databases, model orchestration and enterprise-grade security, enabling teams to integrate large language models and data sources into chatbots, assistants and process automations without deep ML expertise.
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