出海雷达

AI-Native Software Engineer, QA Automation

AI开发工程限定地区（需当地身份）
公司Zenara Health
薪资未公开
工作地点India
地域资格限定地区（需当地身份）
时区要求日间重叠约 6 小时，基本正常作息
用工类型Full Time
发布时间今天
数据来源Himalayas
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注意地域限制：该职位明确限定在 India 招聘。如果你是位于中国大陆的求职者，通常需要当地工作身份才能投递，或需与雇主确认是否接受独立合同（Contractor）形式合作。
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智能化软件原生工程师，QA 自动化 中到高级人员 禅拉医疗地点：远程 - 印度类型：全职工作时间：所有会议和现场合作都在美国太平洋时间大约下午6点至上午12点之间进行。该窗口需要参加。剩下的工作是你的计划。承诺：每周大约50小时行业：医疗技术经验优先但不是强制性的公司关于禅拉医院是一个科技为导向的心理健康组织，为行为卫生诊所构建临床软件。我们的产品包括评估和 Zenflow,支持评估，护理管理，临床工作流程和运营交付。我们是一个初创公司，而不是部门。我们正在建立一个 AI 原生工程团队，质量必须与开发相同的速度扩展。关于角色 我们正在寻找专注于 QA 自动化的人工智能软件工程师。这不是手动测试作用。这不是一个简单通过测试案例点击的人员的角色。这是一个构建系统的软件工程角色，证明产品是否有效。我们的开发人员使用 AI 代理建造。这意味着代码可以比传统 QA 能够手动审查更快来。我们需要一个 QA 自动化工程师，能够构建测试带，自动检查，合成数据，测试环境和经纪人驱动的 QA 工作流程，跟随代理速度发展。这里是测试：你可以建造测试系统，种植真正的错误，证明测试抓住它，并解释什么还未验证？我们不寻找签署工作的人，因为它看起来很好。我们正在寻找要求证据的人。我们的 AI-Native QA Vision QA 在禅拉不是下游门。这是一个工程平台。我们正在建设一个系统，产品要求可以成为测试计划，测试数据，自动浏览器和 API 检查，证据报告，故障路由和释放信心与代理帮助创建和运行工作，工程师管理质量。你将协助设计和操作这个 QA 系统，包括：·浏览器及 API 测试运营者 ·合成测试数据 ·孤立的测试环境 · 需要进行测试的工作流程 · 每个变化的自动检查 · 经理驱动的 QA 工作流程 · 发布的证据生成 · Flaky 测试减少 · 失败路由和调试 · 质量指标和释放信任目标不是自动化的

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AI-Native Software Engineer, QA Automation — Mid to Senior | Zenara Health
Location: Remote - India
Type: Full-time
Working Hours: All meetings and live collaboration happen between approximately 6:00 PM and 12:00 AM IST, overlapping US Pacific Time. Attendance in that window is required. Your remaining work is yours to schedule.
Commitment: Approximately 50 hours per week
Industry: Healthcare technology experience is preferred but not mandatory
About the Company
Zenara Health is a technology-oriented mental healthcare organization building clinical software for behavioral-health clinics. Our products include Assess and Zenflow, which support assessments, care management, clinical workflows, and operational delivery .
We are a startup, not a department. We are building an AI-native engineering team where quality must scale at the same speed as development.
About the Role
We are looking for an AI-Native Software Engineer focused on QA Automation.
This is not a manual testing role.
This is not a role for someone who simply clicks through test cases.
This is a software engineering role for someone who builds systems that prove whether the product works.
Our developers build with AI agents. That means code can arrive faster than traditional QA can review it by hand. We need a QA automation engineer who can build the testing harness, automated checks, synthetic data, test environments, and agent-driven QA workflows that keep up with agent-speed development .
Here is the test: can you build a test system, seed a real bug, prove the test catches it, and explain what is still unverified?
We are not looking for someone who signs off work because it “looks fine.” We are looking for someone who demands evidence.
Our AI-Native QA Vision
QA at Zenara is not a downstream gate. It is an engineering platform.
We are building toward a system where product requirements can become test plans, test data, automated browser and API checks, evidence reports, failure routing, and release confidence — with agents helping create and run the work, and engineers governing the quality.
You will help design and operate this QA system, including:

  • Browser and API test runners
  • Synthetic test data
  • Isolated test environments
  • Requirement-to-test workflows
  • Automated checks on every change
  • Agent-driven QA workflows
  • Evidence generation for releases
  • Flaky-test reduction
  • Failure routing and debugging
  • Quality metrics and release confidence

The goal is not to automate bad testing. The goal is to build a system that catches real defects before customers do.
What You’ll Do

  • Build and maintain the QA automation harness across browser, API, data, and workflow layers.
  • Create automated checks that run on every meaningful change.
  • Build synthetic test data and isolated test environments.
  • Own the UAT/QA automation pipeline from requirement to test plan to run to evidence.
  • Use AI agents to help generate, execute, improve, and debug tests.
  • Build independent agent-driven QA workflows that test the work of code-writing agents.
  • Prove tests work by reintroducing known bugs and showing they fail correctly.
  • Reduce flaky tests and distinguish real product failures from environment noise.
  • Produce reliable QA evidence for releases.
  • Work with engineering and product to define quality gates for critical user journeys.

What We’re Looking For

  • Strong software engineering ability: you can design, write, debug, and maintain code.
  • Experience with test automation using Playwright, Cypress, API testing frameworks, or similar tools.
  • CI/CD experience and comfort wiring tests into build and release workflows.
  • Practical experience using AI agents to create or improve tests.
  • Strong judgment about whether a test actually proves anything.
  • Ability to seed a bug, run a test, and demonstrate that the test catches the failure.
  • Comfort debugging flaky tests, environment issues, and test data problems.
  • Skeptical, evidence-driven thinking.
  • Clear communication around risk, coverage, and release confidence.

Our Current Stack

  • TypeScript
  • Node.js, Hono
  • React
  • Prisma, PostgreSQL
  • Redis, BullMQ
  • Playwright
  • Azure AKS, Blob Storage, Azure Monitor / OpenTelemetry
  • Terraform, Helm, CI pipelines
  • Medplum / FHIR
  • Flowable
  • Claude Code, Codex
  • Dify-based AI workflows

What “AI-Native QA” Means for This Role
For this role, being AI-native means you can:

  • Use agents to generate test ideas, test code, and edge cases.
  • Review agent-created tests critically.
  • Build workflows where agents test independently from the agents that wrote the code.
  • Use AI to analyze failures and propose debugging paths.
  • Create repeatable quality gates that scale with development speed.
  • Measure whether AI-generated tests are actually useful.
  • Avoid false confidence from tests that pass without proving behavior.

The point is not to have more tests. The point is to have trustworthy evidence.
Nice to Have

  • Healthcare or regulated-software experience.
  • Experience testing AI-powered features, including output quality.
  • Experience building test environments and synthetic data.
  • Performance or load testing experience.

What Success Looks Like
In your first 30 days, you will map the current QA setup, agree the highest-risk user journeys, and deliver one automated end-to-end QA lane that runs on every change and catches a known bug .
Within your first 90 days, key journeys for Assess and Zenflow will be covered automatically, every release will have QA evidence, and flaky-test rate and run time will be measured and improving .
Over time, success means building a mature agentic QA platform where escaped defects are low and QA throughput keeps pace with agent-speed development .
Outside required collaboration, we care about output, reliability, and quality.
Compensation and Benefits

  • Competitive compensation, based on demonstrated ability and experience
  • Fully remote work
  • Equipment allowance
  • Direct access to the founder and Head of Engineering
  • The opportunity to build the QA function as an engineering platform from the ground up

Who Will Thrive in This Role
This role is well suited to someone who:

  • Thinks like an engineer, not a manual checklist executor.
  • Enjoys building tools, systems, and automation.
  • Is naturally skeptical and evidence-driven.
  • Cares about proving behavior, not just producing green checkmarks.
  • Wants to make QA scale with AI-native development.
  • Likes working in fast-moving, ambiguous startup environments.

You may struggle if you only have manual testing experience, cannot code, sign off work without evidence, or write tests that pass regardless of whether the product is correct .
If you want to build the quality system for an AI-native software team, we would love to hear from you.
Originally posted on Himalayas

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