Site Reliability Engineering AI Evaluator

AI Trainer Jobs; United States; Remote; USD 83,000 - 165,000; Part time

Benefits offered by this job

Remote work

Contractor position

Flexible hours

Job summary

AI Trainer Jobs is seeking a remote Site Reliability Engineering AI Evaluator to analyze production code, debug traces, and model outputs against correctness standards. You will reproduce failures, write unit tests, and explain fixes so the modeling team can target gaps.

The role emphasizes evaluating generated code for compilation, tests, and edge cases, working with experienced engineers, and judging outputs with a rubric. This is a contractor position with flexible hours.

Qualifications

• Strong day-job engineering experience.
• Ability to read, run, and debug unfamiliar code.
• Experience writing concise unit tests that capture a single failure mode.
• Clear written reasoning to convince a senior engineer.
• Reliable async availability for at least 10 hours per week.
• Prior code-review or technical-interview-grading experience is a plus.

Responsibilities

• Run and reproduce candidate code outputs in a sandboxed environment.
• Grade cloud & platform 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.

Skills

Engineering experience

Unit tests

Concise written reasoning

Async availability

Code review

Tools

pytest

Jest

JUnit

Job description

About the role

Site Reliability 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 Site Reliability Engineering AI Evaluator assignments.
• Grade cloud & platform 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.

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 Site Reliability 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
• Cloud & platform engineering
• Software engineering and computer use
• AI evaluation
• Rubric writing
• Expert review