Frontend Engineering AI Evaluator

AI Trainer Jobs; United States; Remote; USD 55,000 - 96,000; Part time

Job summary

AI Trainer Jobs is seeking a Frontend Engineering AI Evaluator for a remote, independent contractor role. You will reproduce candidate code, grade frontend solutions for correctness and edge cases, and write minimal failing tests to reveal model gaps.

You will document rationale and tag failure modes with severity, supporting the modeling team’s improvements. The role is remote with hourly compensation; candidates must be able to work about 10 hours per week and be eligible to work from the

Qualifications

• Strong day-job engineering experience — you can read, run, and debug unfamiliar code for Frontend Engineering AI Evaluator work.

Responsibilities

• Run and reproduce candidate code outputs in a sandboxed environment for Frontend Engineering AI Evaluator assignments.
• Grade frontend engineering solutions for correctness, style, and edge-case handling.
• Write minimal failing tests that demonstrate the bug a model output missed.
• Compare paired solutions and rank them with a written rationale tied to the rubric.
• Tag failure modes (compile error, runtime crash, off-by-one, security issue) with severity scores.

Skills

Code review

Debugging

Unit testing

Frontend engineering

AI evaluation

Rubric writing

Expert review

Reading & running unfamiliar code

async communication

Tools

pytest

Jest

JUnit

Go test

RSpec

Job description

About the role

Frontend Engineering AI Evaluator is a remote engineering review track for evaluating production code, debugging traces, and developer-facing AI outputs against real-world correctness standards. Reviewers reproduce failures, write the unit test the model should have written, and explain the fix so the modeling team can target the gap.
Engineering model quality lives or dies on whether the generated code actually compiles, passes tests, and handles edge cases. AuraOne pairs experienced engineers with the modeling team to grade outputs the way a code reviewer would.
Judge generated code and software engineering agents. Read their debugging traces.

Responsibilities

• Run and reproduce candidate code outputs in a sandboxed environment for Frontend Engineering AI Evaluator assignments.
• Grade frontend engineering solutions for correctness, style, and edge-case handling.
• Write minimal failing tests that demonstrate the bug a model output missed.
• Compare paired solutions and rank them with a written rationale tied to the rubric.
• Tag failure modes (compile error, runtime crash, off-by-one, security issue) with severity scores.

Role details

Track Code review & evaluation Work model Remote · Independent specialist contractor Compensation Hourly rate confirmed after the interview process. Eligible from US

What you should bring

• Strong day-job engineering experience — you can read, run, and debug unfamiliar code for Frontend Engineering AI Evaluator work.
• Comfort writing concise unit tests that capture a single failure mode.
• Familiarity with a testing framework. pytest, Jest, or JUnit. Go test, RSpec, or whatever you use.
• Clear written reasoning — your review note has to convince another senior engineer.
• Reliable async availability for at least 10 hours per week.
• Prior code-review or technical-interview-grading experience is a plus.

Role signals

Example tasks

• Reproduce a generated engineering solution to a coding task, run the test suite, and grade it.
• Write the smallest failing test that demonstrates a model's edge-case bug.
• Compare two paired solutions and rank them with a written rationale tied to the rubric.
• Triage a security issue surfaced by a model and document the patch the model should have produced.

Useful experience

• Open-source contributions or a public portfolio that demonstrates production-quality code.
• Experience with the target language's standard tooling, linters, and idiomatic style guides.
• Familiarity with security-review checklists (OWASP, CWE) and AppSec patterns.

Compensation and schedule

Hourly rate confirmed after the interview process.
Expected arrangement: contractor , with program-defined task volume and review pacing. Placement depends on current program demand and reviewer confirmation.

Skills used in matching

• Code review
• Debugging
• Unit testing
• Software engineering judgment
• Frontend engineering
• Software engineering and computer use
• AI evaluation
• Rubric writing
• Expert review

Application boundary

Creating a specialist profile records your experience and preferences. Starting role intake is a separate action that attaches this role to your candidate record.

Specialist intake

The intake preserves your chosen role, the visible terms, and source attribution for reviewer context.

• 01 Confirm profile and eligibility details.
• 02 Attach this role deliberately.
• 03 Receive a human review decision or follow-up.
  Placement timing depends on program demand and reviewer confirmation.