Senior Python Code Reviewer (Docker Required)

About OpenTrain

OpenTrain is the #1 platform for finding and building careers in AI training and data labeling. We help people start and grow careers teaching AI by connecting contributors with real projects, letting you build a profile, apply quickly, and work on the human side of how modern AI systems get trained.

About AI training work

AI training (also called data labeling or human feedback work) is the human layer behind modern models: people annotate, evaluate, and validate examples that shape model behavior. Work in this industry is highly flexible, often remote and part-time, and ranges from simple labels to specialist technical review for code, medical, or legal content.

The role — what you'll do

You will audit annotator evaluations of AI-generated Python code and perform proof-of-work validation in isolated environments. Your reviews ensure each code snippet follows the prompt, executes correctly, and meets security best practices while preserving label quality and project guidelines.

• Run containerized checks to reproduce and validate code outputs and behavior.
• Verify that code matches prompts, handles edge cases, and includes adequate tests where applicable.
• Identify security issues (injection, unsafe deserialization, race conditions, permission errors) and report them clearly.
• Catch and correct rating errors from annotators, using project checklists and rubrics.
• Provide concise, constructive written feedback and documentation for annotators and project leads.

What a typical day looks like

As a part-time contractor (under 20 hours/week) you’ll pick up review batches, spin up isolated environments (Docker), run proofs-of-work, score each item against a rubric, and file clear QA notes or tickets for problematic items. You will work within a structured QA workflow and communicate findings in written form.

• Receive a batch and checklist from the annotation platform.
• Build/run containers to execute or sandbox code snippets and tests.
• Record execution results, pass/fail outcomes, and security concerns.
• Submit rubric scores, short feedback comments, and any follow-up tickets.

Requirements (must-haves)

All of the following are required and will be verified during screening.

• Minimum seven years of hands-on Python experience (compatible with Python <= 3.12), with demonstrable mastery of advanced syntax and idioms.
• Expertise in debugging and testing: pytest or unittest, coverage, profiling, and designing edge-case tests.
• Strong Docker skills: build, run, exec into containers, multi-stage images, volume mounts, and troubleshooting containerized environments.
• Secure-coding mindset: identify injection risks, unsafe deserialization, race conditions, and permission issues.
• Experience validating code via sandbox or container execution (proof-of-work checks).
• Familiarity with structured QA workflows: checklists, rubric scoring, and ticketing tools.
• Clear written English communication (concise, constructive feedback; roughly B2+ CEFR).
• Remote reliability: stable internet and environment, ability to meet milestone discipline for contract work.

Preferred qualifications

These are not required but will help your application stand out.

• Experience evaluating LLM outputs or working on RLHF/model-evaluation projects.
• Familiarity with annotation platforms and labeling QA tooling.
• History of mentoring reviewers, contributing to guidelines, or improving annotation quality processes.

How it works, schedule, and pay

This is a contract, part-time role for contributors worldwide. You will be paid hourly at the listed rate and work under a structured QA process. OpenTrain connects you to projects and supports your onboarding but the role itself is a contractor position.

• Employment type: Contractor, Part-time.
• Time commitment: Less than 20 hours per week (flexible scheduling within project deadlines).
• Pay: USD $18.00 per hour (PAY_PER_HOUR).
• Location: Remote — open to candidates worldwide.
• Labeling work: AI-generated Python code response annotation; dataType: COMPUTER_CODE_PROGRAMMING.

How to apply

If you meet the must-have requirements, apply with a brief summary of relevant Python and Docker experience, examples of past code-review or QA work (links or portfolio entries if available), and your typical weekly availability. Be prepared for a technical screening that will probe the competencies listed under requirements.