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.
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