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
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