Job Description:

Responsibilities:

Model Quality Assessment: Evaluate the quality of AI model responses that include code, machine learning, AI, identifying errors, inefficiencies, and non-compliance with established standards.
Code Annotation and Labeling: Accurately generate, annotate and label code snippets, algorithms, and technical documentation according to project-specific guidelines.
Review and Feedback: Provide detailed, constructive feedback on model and other outputs
Comparative Analysis: Compare multiple outputs and rank them based on criteria such as correctness, efficiency, readability, and adherence to programming best practices.
Data Validation: Validate and correct datasets to ensure high-quality data for model training and evaluation.
Collaboration: Work closely with data scientists and engineers to identify new annotation guidelines, resolve ambiguities, and contribute to the overall project strategy.

Qualifications:

Strong background in software engineering/development, computer science, ML/AI, or related technical field, with a keen eye for detail and a passion for data accuracy
Programming Proficiency: Demonstrated expertise in:
- Python (must-have) and at least one or more common programming languages such as: JavaScript, Rust, Node.js, Typescript, C, C++, Shell
- (Bonus points) At least 1 or more less common programming languages such as: Rust, Shell, Go, Ruby, Swift, PHP, Kotlin

Knowledge of web technologies & frameworks
- Web Scraping, API integration,
- HTML/CSS/JavaScript
- Web application development (e.g. Flask)
- Frontend (e.g. React) and backend (e.g. Node.js) development
Machine Learning & Artificial Intelligence
- Machine Learning (General concepts, model development, experimentation, training, evaluation)
- Deep Learning (General, frameworks like TensorFlow, PyTorch, JAX, Keras, neural networks, CNNs, RNNs, transformer architecture, LSTM)
- Natural Language Processing (NLP)
- Reinforcement Learning (e.g., PPO, Q-learning, policy gradients, A2C, DQN, AlphaZero)
- Computer Vision (e.g., image processing, analysis, instance segmentation, OCR, deepfake detection)
- Game AI (Specific AI for intelligent opponents, understanding game states, actions, rewards)
Data Science & Engineering
- Data Analysis & Manipulation (including Pandas, Matplotlib, Seaborn, NumPy, statistical analysis, general data processing, visualization libraries)
- Database Management (SQL, NoSQL, SQLite, data storage
Algorithms & Mathematics
- Algorithms (General and specific like Monte Carlo Tree Search (MCTS), A* pathfinding, Sudoku solving, Collatz sequence, optimization, combinatorial problems)
Software Engineering Practices & Tools
- Version Control (Git/GitHub)
- Coding Best Practices: A solid understanding of clean code principles, software design patterns, and debugging techniques.
Attention to Detail: Meticulous attention to detail and the ability to follow complex, multi-step instructions precisely.
Problem-Solving: Strong analytical and problem-solving skills to evaluate and troubleshoot complex coding solutions.
Communication: Excellent written communication skills to provide clear, concise, and actionable feedback
Proactiveness: Willingness to challenge the status quo to conduct a given task and achieve the end goal

Preferred Qualifications:

Experience with AI/ML concepts, particularly with large language models (LLMs) and code generation.
Familiarity with various programming paradigms (e.g., object-oriented, functional).
Experience with code review in a professional or academic setting.
Experience in data annotation or similar quality assurance roles.

Expert Coding Raters