Remote ML Code Reviewer & Debugging Engineer

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

Job summary

AI Trainer Jobs is seeking a remote ML Engineer contractor to evaluate production code, debug traces, and AI outputs for real-world correctness. You will reproduce failures, write the unit tests the model should have written, 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 ranking results with rubric-based rationale.

Qualifications

• Strong day‑job engineering experience — you can read, run, and debug unfamiliar code for ML Engineer 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.

Responsibilities

• Run and reproduce candidate code outputs in a sandboxed environment for 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.

Skills

Code review

Debugging

Unit testing

ML engineering

AI model evaluation

Tools

pytest

Jest

JUnit

Go test

RSpec

Job description

AI Trainer Jobs is seeking a remote ML Engineer contractor to evaluate production code, debug traces, and AI outputs for real-world correctness. You will reproduce failures, write the unit tests the model should have written, 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 ranking results with rubric-based rationale.