Data QA Engineer

Location: Remote; eligible from the United States.

Company

Builds AI models and uses human data specialists to evaluate software engineering outputs.

What you will do

• Run and reproduce generated code outputs in a sandboxed environment.
• Evaluate engineering review solutions for correctness, style, and edge-case handling.
• Write minimal failing unit tests that demonstrate missed bugs.
• Compare paired solutions and rank them with written rationale tied to a rubric.
• Tag failure modes, including compile errors, runtime crashes, off-by-one errors, and security issues, with severity scores.
• Document recurring code-generation failures and calibrate reviews against gold-standard examples to maintain inter-rater agreement.

Requirements

• Strong professional engineering experience with the ability to read, run, and debug unfamiliar code.
• Ability to write concise unit tests that capture a single failure mode.
• Familiarity with a testing framework such as pytest, Jest, JUnit, Go test, or RSpec.
• Clear written reasoning that can convince another senior engineer.
• Reliable asynchronous availability for at least 10 hours per week.

Nice to have

• Prior code-review or technical-interview-grading experience.
• Open-source contributions or a public portfolio demonstrating production-quality code.
• Experience with standard tooling, linters, and idiomatic style guides for the target language.
• Familiarity with OWASP, CWE, security-review checklists, and application-security patterns.

Culture & Benefits

• Remote work as an independent specialist contractor.
• Program-defined task volume and review pacing.
• Expected availability of at least 10 hours per week.
• Placement depends on current program demand and reviewer confirmation.

Hiring process

• Confirm profile and eligibility details during specialist intake.
• Attach the selected role to the application.
• Receive follow-up if the profile fits a current program; placement timing depends on program demand and reviewer confirmation.