Fullstack Engineer-GEN AI

Location: Remote; eligible from the United States

Company

hirify.global Human Data operates a specialist program for evaluating human data, coding, software engineering, and AI-agent outputs.

What you will do

• Run and reproduce generated code outputs in a sandboxed environment.
• Grade full-stack engineering solutions for correctness, style, and edge-case handling.
• Write minimal failing unit tests that demonstrate missed bugs.
• Compare paired solutions, rank them against a rubric, and provide written reasoning.
• Tag failure modes such as 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.

Requirements

• Strong professional engineering experience with the ability to read, run, and debug unfamiliar code.
• Comfort writing 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 suitable for review by 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.
• Expected contractor arrangement with program-defined task volume and review pacing.
• Hourly compensation, with the rate confirmed after the interview process.
• Placement depends on current program demand and reviewer confirmation.

Hiring process

• Submit an application for direct review by the recruiting team.
• Complete human review after intake and confirm profile and eligibility details.
• Placement and follow-up depend on profile fit, current program demand, and reviewer confirmation.