Remote AI Code Review Engineer

AI Trainer Jobs; United States; Remote; USD 91,000 - 129,000; Full time

Benefits offered by this job

Remote work

Job summary

AI Trainer Jobs seeks a Project Controls Engineer for a remote, contractor-based role evaluating production code, debugging traces, and AI outputs against real-world correctness standards. You will reproduce failures, write unit tests, and explain fixes to the modeling team to target gaps.

You’ll work with code-review-oriented tasks in a sandboxed environment, grade solutions for correctness and edge cases, and craft minimal failing tests.

Qualifications

• Strong day-job engineering experience — you can read, run, and debug unfamiliar code for Project Controls Engineer work.
• Comfort writing concise unit tests that capture a single failure mode.
• Familiarity with a testing framework (pytest, Jest, JUnit, Go test, RSpec).
• 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.

Responsibilities

• Run and reproduce candidate code outputs in a sandboxed environment for Project Controls Engineer assignments.
• Grade project controls engineer engineering review 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

Testing frameworks

Written reasoning

Async availability

Code-review experience

Tools

pytest

Jest

JUnit

Go test

RSpec

Job description

AI Trainer Jobs seeks a Project Controls Engineer for a remote, contractor-based role evaluating production code, debugging traces, and AI outputs against real-world correctness standards. You will reproduce failures, write unit tests, and explain fixes to the modeling team to target gaps.

You’ll work with code-review-oriented tasks in a sandboxed environment, grade solutions for correctness and edge cases, and craft minimal failing tests.