Remote MLOps LLM Systems Reviewer (Code Quality)

AuraOne; United States; On-site; USD 124,000 - 165,000; Full time

Job summary

AuraOne is seeking a remote contractor for an MLOps Engineer, LLM Systems role focused on evaluating production code, debugging traces, and developer-facing AI outputs for correctness. You will reproduce failures, write tests, and explain fixes so the modeling team can target gaps.

Responsibilities include running candidate code in sandboxes, grading solutions for correctness and edge cases, and documenting recurring failures to improve model quality. Strong testing discipline is essential.

Qualifications

• Strong ability to read, run, and debug unfamiliar code for MLOps Engineer, LLM Systems work.
• Comfort writing concise unit tests that capture a single failure mode.
• Familiarity with a testing framework such as pytest, Jest, or JUnit.
• Clear written reasoning in review notes to persuade senior engineers.
• 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 MLOps Engineer, LLM Systems (Serving, GPU Kernels, Profiling) assignments.
• Grade mlops engineer llm systems 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.
• Tag failure modes with severity scores (compile error, runtime crash, off-by-one, security issue).
• Document recurring code-generation failures so the modeling team can target them.
• Calibrate against gold-standard reviews to keep inter-rater agreement above target.

Skills

Code review

Debugging

Unit testing

Software engineering judgment

MLOps Engineer LLM Systems engineering

Job description

AuraOne is seeking a remote contractor for an MLOps Engineer, LLM Systems role focused on evaluating production code, debugging traces, and developer-facing AI outputs for correctness. You will reproduce failures, write tests, and explain fixes so the modeling team can target gaps.

Responsibilities include running candidate code in sandboxes, grading solutions for correctness and edge cases, and documenting recurring failures to improve model quality. Strong testing discipline is essential.