AI/ML Engineer

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.