Remote AI/ML Code Reviewer & Debug Specialist

AI Trainer Jobs; United States; Remote; USD 165,000 - 193,000; Full time

Benefits offered by this job

Remote work

Job summary

AI Trainer Jobs seeks an AI/ML Engineer for a remote engineering review track to evaluate production code, debug traces, and developer-facing AI outputs against real-world correctness standards. Reviewers reproduce failures, write tests, and explain fixes so the modeling team can target gaps.

Responsibilities include running and reproducing candidate code in a sandbox, grading solutions for correctness and edge cases, writing minimal failing tests, and comparing submissions with a rubric.

Qualifications

• Strong day-job engineering experience and ability to read, run, and debug unfamiliar code.
• Ability to write concise unit tests for a single failure mode.
• Familiarity with a testing framework (pytest, Jest, JUnit, Go test, RSpec, etc.).
• Clear written reasoning to persuade 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 for AI/ML Engineer assignments.
• Grade ml 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

Engineering experience

Unit tests

Testing framework

Written reasoning

Async availability

Code-review

Tools

pytest

Jest

JUnit

Go test

RSpec

Job description

AI Trainer Jobs seeks an AI/ML Engineer for a remote engineering review track to evaluate production code, debug traces, and developer-facing AI outputs against real-world correctness standards. Reviewers reproduce failures, write tests, and explain fixes so the modeling team can target gaps.

Responsibilities include running and reproducing candidate code in a sandbox, grading solutions for correctness and edge cases, writing minimal failing tests, and comparing submissions with a rubric.