Python Developer - Tajikistan

## About SME Careers As AI systems become more sophisticated, they rely on human knowledge to better understand context and the world. At SME Careers, we partner with leading AI labs to improve how the next generation of intelligent systems learn, reason, and communicate. SME Careers is a fast-growing AI data services company and subsidiary of SuperAnnotate that provides AI training data to many of the world's top AI companies and foundation model labs. Your expertise as an Python Developer will directly help improve the world's premier AI models. Your expertise will play an important role in shaping the future of AI. ## About the Role In this hourly, remote role, you will review and compare AI-generated Python-focused responses, evaluate the quality of the reasoning and step-by-step problem-solving, and provide structured expert feedback that helps models produce accurate, logical, and clearly explained answers. You will assess solutions for correctness and clarity, spot conceptual and methodological errors, fact-check technical claims, and write high-quality explanations and reference solutions when needed. You will also rate and rank multiple candidate responses against a prompt-specific rubric to ensure consistent, useful training signals. This position is with SME Careers, a fast-growing AI data services company and subsidiary of SuperAnnotate that provides AI training data to many of the world's largest AI companies and foundation model labs. Your contributions will directly help improve the world's premier AI models by strengthening the quality and reliability of Python-related training and evaluation data. ## What You'll Do - Develop AI Training Content: Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects. - Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance. - Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases. - Write C1-level English evaluation feedback that clearly explains rating decisions, highlights Python-specific issues, and provides actionable corrections. - Review AI-generated Python solutions for correctness, edge cases, and adherence to Pythonic conventions (e.g., readability, naming, and idiomatic constructs). - Provide reference explanations for why a Python approach is correct or incorrect (e.g., mutability, scoping, iteration protocols, exceptions, and data model behavior). - Identify recurring failure patterns in generated Python answers (e.g., mutable defaults, late-binding closures, shadowing built-ins, incorrect exception handling) and document them as reusable reviewer notes. - Develop lightweight verification checks in Python (e.g., small input/output examples and minimal unit-test style assertions) to validate model-proposed solutions used in training datasets. ## What You Bring - 2–5 years of relevant professional experience as a Python Developer (Python 3.x). - Minimum Bachelor's degree in Computer Science, Software Engineering, or a closely related field or equivalent practical experience. - English proficiency: Minimum C1 level. - Previous experience with AI data training, annotation, or evaluating AI-generated content is strongly preferred. - Strong command of core Python concepts: data model (dunder methods), mutability, scoping/name binding, iteration protocols, exceptions, and common standard-library usage. - Ability to evaluate correctness and robustness of Python solutions, including reasoning about edge cases and common pitfalls (e.g., mutable defaults, reference vs copy, integer vs float behavior). - Practical understanding of Python performance considerations (I/O vs CPU, algorithmic complexity, generators vs materialized collections) sufficient for reviewing and comparing solutions. - Ability to explain Python reasoning clearly and concisely in writing (why a solution works, why it fails, and what a safer alternative would be). - Detail-oriented and self-directed working style suitable for remote, flexible-hours contract work with rubric-based quality standards. ## Application & Onboarding Process 1. Application review (CV/LinkedIn plus a short overview of your Python experience). 2. Short Python screening focused on core language fundamentals and practical debugging judgment. 3. Paid qualification tasks to confirm you can apply a rubric consistently when reviewing Python-related responses. 4. Contract setup and onboarding (tool access, guidelines, and calibration examples). 5. Start working flexible hours on available tasks; quality audits and periodic recalibration help keep scoring consistent. ## Job Details - Company: SME Careers (subsidiary of SuperAnnotate) - Title: Python Developer (AI training & evaluation) - Employment type: Contract (hourly) - Location type: Remote - Country eligibility: Tajikistan (TJ) - Schedule: Flexible hours; part-time, task-available basis - Estimated pay range: $5.25–$7.35 USD/hour - Openings: 1 ## Why Join Us - Flexible remote work that fits around other commitments. - Clear rubrics and examples that make expectations explicit and measurable. - Work that strengthens real-world Python correctness and reasoning in widely used AI systems. - Exposure to a variety of Python problem types (debugging, edge cases, performance reasoning, and explanation quality).

### Your Profile
- 2–5 years of relevant professional experience as a Python Developer (Python 3.x).
- Minimum Bachelor's degree in Computer Science, Software Engineering, or a closely related field or equivalent practical experience.
- English proficiency: Minimum C1 level.
- Previous experience with AI data training, annotation, or evaluating AI-generated content is strongly preferred.
- Strong command of core Python concepts: data model (dunder methods), mutability, scoping/name binding, iteration protocols, exceptions, and common standard-library usage.
- Ability to evaluate correctness and robustness of Python solutions, including reasoning about edge cases and common pitfalls (e.g., mutable defaults, reference vs copy, integer vs float behavior).
- Practical understanding of Python performance considerations (I/O vs CPU, algorithmic complexity, generators vs materialized collections) sufficient for reviewing and comparing solutions.
- Ability to explain Python reasoning clearly and concisely in writing (why a solution works, why it fails, and what a safer alternative would be).
- Detail-oriented and self-directed working style suitable for remote, flexible-hours contract work with rubric-based quality standards.

### Key Responsibilities
- Develop AI Training Content: Create detailed prompts in various topics and responses to guide AI learning, ensuring the models reflect a comprehensive understanding of diverse subjects.
- Optimize AI Performance: Evaluate and rank AI responses to enhance the model's accuracy, fluency, and contextual relevance.
- Ensure Model Integrity: Test AI models for potential inaccuracies or biases, validating their reliability across use cases.
- Write C1-level English evaluation feedback that clearly explains rating decisions, highlights Python-specific issues, and provides actionable corrections.
- Review AI-generated Python solutions for correctness, edge cases, and adherence to Pythonic conventions (e.g., readability, naming, and idiomatic constructs).
- Provide reference explanations for why a Python approach is correct or incorrect (e.g., mutability, scoping, iteration protocols, exceptions, and data model behavior).
- Identify recurring failure patterns in generated Python answers (e.g., mutable defaults, late-binding closures, shadowing built-ins, incorrect exception handling) and document them as reusable reviewer notes.
- Develop lightweight verification checks in Python (e.g., small input/output examples and minimal unit-test style assertions) to validate model-proposed solutions used in training datasets.