STEM Leads for GenAI Data Role Overview The Team Lead for [Maths/Physics/Chemistry/Biology/Coding/Finance] is a pivotal role that bridges deep academic expertise with the practical application of AI fine-tuning. You will be responsible for leading a team of SMEs, setting the gold standard for quality, and acting as the ultimate authority for your subject. Your primary objective is to guide your team in creating and curating the high-quality data that shapes the "brain" of our AI, ensuring its responses are not just correct, but also insightful, well-structured, and tailored to the learner's level. This is a role for a passionate educator and a meticulous expert who is excited by the prospect of teaching millions at scale through technology. Profile of the Ideal Candidate I. Core Responsibilities Team Leadership & Management: Define project scope and data creation requirements based on inputs from research and engineering teams Efficiently allocate team members and resources to specific data creation and evaluation projects, optimizing for expertise and workload. Lead, mentor, and manage a team of Subject Matter Experts, fostering a collaborative and high-performance culture, based on a thorough understanding of team capabilities Develop and manage team workflows, set clear performance goals, and provide regular, constructive feedback. Conduct final quality assurance and review of the team's work, ensuring consistency and adherence to the highest standards. Establish and report on key performance indicators (KPIs) for team productivity, data quality, and project timelines. Onboard and train new SMEs on our quality guidelines, tools, and fine-tuning methodologies. Subject Matter & Content Strategy: Act as the ultimate arbiter for content accuracy and quality in [Maths/Physics/Chemistry/Biology/Coding/Finance], from foundational concepts to advanced, post-graduate level topics. Develop, refine, and enforce a comprehensive quality rubric and style guide for AI responses. This includes standards for accuracy, clarity, depth, step-by-step explanations, and pedagogical effectiveness. AI Model Fine-Tuning & Evaluation: Collaborate closely with the AI/ML engineering team to translate subject matter nuances into actionable feedback for model improvement. Analyze the AI model's performance on [Maths/Physics/Chemistry/Biology/Coding/Finance] queries, identifying systemic weaknesses, biases, or recurring error patterns. Proactively identify and resolve operational bottlenecks or technical issues within the data creation workflow, collaborating with engineering and product teams as needed Design and oversee the creation of sophisticated evaluation datasets ("exam papers" for the AI) to benchmark model progress and accuracy. Guide the team in advanced prompt engineering and data creation techniques to address complex, multi-step problems and conceptual questions. Cross-Functional Collaboration: Serve as the primary point of contact between your subject team and other departments, including Product Management, Analytics, Engineering, and other Subject Leads. Communicate complex subject-specific challenges and requirements clearly to a non-expert audience (such as tooling and dashboarding needs) Contribute to the overall strategy for improving the AI's STEM capabilities. II. Required Qualifications & Experience Educational Background: A Ph.D. in [Maths/Physics/Chemistry/Biology/Coding/Finance] or a closely related field is strongly preferred. A Master's degree (M.Sc./M.Tech) from a premier institution (e.g., IIT, NIT, IISc) with an exceptional academic record is the minimum requirement. Deep Subject Expertise: Unassailable, demonstrable expertise across the breadth and depth of your subject, covering syllabi for JEE Mains & Advanced, and major undergraduate/postgraduate curricula. Teaching/Academic Experience: A minimum of 7-10 years of combined experience in teaching, research, or content development. Significant experience teaching for competitive exams (JEE Mains/Advanced) is a massive plus. Leadership Experience: Proven experience (2+ years) in a leadership or mentorship role, managing a team of academics, content creators, or researchers. Exceptional Communication Skills: Flawless written and verbal communication skills. Ability to articulate complex concepts simply and effectively. III. Preferred Qualifications (The "Ideal" Candidate) Ed-Tech Experience: Prior experience working in an educational technology company, especially one focused on AI-driven learning. Published Work: A track record of publications in reputable journals or authorship of acclaimed textbooks or study materials. Data-Driven Mindset: Comfortable using data and analytics to evaluate content quality and model performance. IV. Personal Attributes & Soft Skills Meticulous & Detail-Oriented: An obsession with accuracy and a keen eye for detail. You notice the small errors that others miss. Pedagogical Passion: A genuine passion for education and a deep understanding of what makes an explanation effective and engaging for a learner. Analytical Problem-Solver: You can break down complex problemswhether a physics question or a pattern of AI errors—into manageable parts and devise effective solutions. Highly Organized: Ability to manage multiple priorities and projects in a fast-paced, dynamic environment. Collaborative & Adaptable: Thrives in a team environment and is open to feedback and new ideas. You are excited by the challenge of working at the intersection of academia and technology.
Job Description: [1] 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[2] [3] (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.