初级软件工程师（QA与自动化）
Junior Software Engineer (QA & Automation)
Description
这个职位适合一位对AI和数据驱动产品充满热情的早期职业工程师。你将帮助确保我们系统的准确性、一致性和可靠性，同时从资深工程师那里学习。作为一支小型协作团队的一员，你将测试、验证并自动化使复杂流程变得简单、可重复和可靠的流程。
灵活：每周20–35+小时
Core Responsibilities
Manual QA & Validation
- 测试软件流水线和AI模型输出的准确性、一致性和稳定性。
- 开发和维护自动化验证脚本和回归测试套件。
- 维护和整理测试数据集，以确保覆盖正常、边缘和故障场景。
- 与产品和工程团队合作，协助定义和记录测试计划、验收标准和QA结果。
Automated Testing
- 使用PyTest或Jest等框架编写自动化单元和集成测试。
- 将自动化测试集成到CI/CD流水线（例如GitHub Actions、Jenkins）中，实现可重复的QA工作流程。
- 在资深工程师的指导下监控测试结果并排查失败原因。
Configuration & Environment Management
- 按照既定流程应用和验证代码和流水线配置。
- 维护配置文件、环境变量和模式更新，跨测试环境进行管理。
- 在资深工程师的指导下，支持新客户的数据显示映射、模式定义和参数配置设置。
- 验证新客户的配置和示例输出的准确性和完整性。
Lightweight Development
- 作为QA反馈的一部分，实施小的bug修复和代码改进。
- 参与代码审查，协助重构或文档编写。
- 协作编写脚本和自动化流程，以简化验证、部署或监控步骤。
- 参与团队QA评审和回顾，以改进流程和自动化覆盖率。
Required Skills & Experience
- 1–3年在QA自动化、软件测试或软件工程方面的专业经验。
- 熟悉Python或类似的脚本语言。
- 熟悉单元测试框架（例如PyTest、Unittest、Mocha/Jest）。
- 基本了解CI/CD工具（例如GitHub Actions、Jenkins、CircleCI）。
- 有Git和现代源代码控制工作流的经验。
- 能够处理JSON模式、API验证和数据驱动测试。
- 能够熟练使用Kubernetes、Docker、AWS、GCP、Azure等云平台和基础设施工具。
查看英文原文
Description
This role is ideal for an early-career engineer excited to work on AI and data-driven products. You’ll help ensure the accuracy, consistency, and reliability of our systems while learning from senior engineers. As part of a small, collaborative team, you’ll test, validate, and automate workflows that make complex processes simple, repeatable, and reliable.
Flexible: 20–35+ hours/week
Core Responsibilities
Manual QA & Validation
- Test software pipelines and AI model outputs for accuracy, consistency, and stability.
- Develop and maintain automated validation scripts and regression test suites.
- Maintain and curate test datasets to ensure broad coverage of normal, edge, and failure scenarios.
- Assist in defining and documenting test plans, acceptance criteria, and QA results with product and engineering teams.
Automated Testing
- Write automated unit and integration tests using frameworks such as PyTest or Jest.
- Integrate automated tests into CI/CD pipelines (e.g., GitHub Actions, Jenkins) for repeatable QA workflows.
- Monitor test results and troubleshoot failures with guidance from senior engineers.
Configuration & Environment Management
- Apply and verify code and pipeline configurations following defined processes.
- Maintain configuration files, environment variables, and schema updates across test environments.
- Support setup of data mappings, schema definitions, and parameter configurations for new customers with guidance from senior engineers.
- Validate new customer configurations and sample outputs for accuracy and completeness.
Lightweight Development
- Implement minor bug fixes and small code enhancements as part of QA feedback.
- Contribute to code reviews and assist in refactoring or documentation.
- Collaborate on scripting and automation to streamline validation, deployment, or monitoring steps.
- Participate in team QA reviews and retrospectives to improve processes and automation coverage.
Required Skills & Experience
- 1–3 years of professional experience in QA automation, software testing, or software engineering.
- Working knowledge of Python or similar scripting languages.
- Familiarity with unit testing frameworks (e.g., PyTest, Unittest, Mocha/Jest).
- Basic understanding of CI/CD tools (e.g., GitHub Actions, Jenkins, CircleCI).
- Experience with Git and modern source control workflows.
- Comfortable working with JSON schemas, API validation, and data-driven testing.
- Comfortable leveraging AI tools to augment and optimize day-to-day tasks.
- Strong attention to detail and process adherence.
- Comfortable working in small, fast-moving technical teams.
Ideal Candidate Traits
- Hands-on and detail-oriented, with the ability to thrive in a fast-moving startup environment.
- A “get it done” attitude and proven track record of taking ownership over workstreams.
- Comfortable managing priorities across multiple operational responsibilities.
- Collaborative and able to communicate effectively with both technical and non-technical stakeholders.
Nice to Haves
- Exposure to AI, ML, or data processing pipelines.
- Experience validating AI or ML model outputs (data extraction, classification, etc.).
- Experience with Docker or cloud-based environments.
- Familiarity with schema validation libraries and data transformation workflows.
Originally posted on Himalayas