REQUIRED SKILLS AND QUALIFICATIONS

• 5+ years of experience in developing and deploying machine learning models in a production environment.
• 8+ years of experience in software development
• Strong understanding of machine learning algorithms (e.g., regression, classification, clustering, reinforcement learning, deep learning) and their applications.
• Proficiency in programming languages such as Python and experience with machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn, Transformers).
• Experience with natural language processing (NLP) techniques and libraries.
• Experience with data processing and storage technologies (e.g., SQL, NoSQL databases, cloud computing platforms).
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration skills.

PREFERRED SKILLS AND QUALIFICATIONS

• Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, or a related field.
• Experience in the sports technology or related field.
• Knowledge of sports biomechanics, physics, and neurology.
• Experience with real-time data processing and analysis.
• Familiarity with wearable sensor data and integration.
• Experience with building and maintaining data pipelines.
• Experience with cloud computing platforms (AWS, GCP, Azure).
• Experience with LLM-based summarization and QA engines.
• Experience with computer vision techniques and libraries (e.g., OpenCV).
• Experience with user interface design and development.

• Design, develop, and deploy core machine learning models for analyzing player performance, including biomechanical analysis, shot trajectory prediction, energy transfer optimization, and cognitive performance assessment.
• Develop and implement natural language processing (NLP) models to ingest, process, and understand external scientific research (sports biomechanics, ballistics, neurology). This includes building a system for summarizing research papers, extracting key insights, and creating a knowledge base.
• Develop and implement personalization algorithms to tailor coaching recommendations based on individual athlete characteristics, performance history, and learning styles.
• Integrate scientific research findings and sports science principles into the AI engine to ensure the accuracy and effectiveness of feedback and training recommendations.
• Develop and implement algorithms for real-time data fusion from various sources, including video streams, wearable sensors (Apple Health, WHOOP, Oura), and environmental data.
• Build and maintain a robust data pipeline for collecting, processing, and storing large datasets of player performance data, scientific literature, and environmental information.
• Implement self-learning and continuous retraining mechanisms for AI models based on user performance and new scientific discoveries.
• Develop AI-generated reports including biomechanical shot analysis, energy transfer metrics, and comparisons to elite athletes.
• Create AI-powered training adjustments and personalized drill recommendations based on player performance, fatigue detection, and form deviations.
• Collaborate with SMEs (Subject Matter Experts) in sports science and coaching to validate AI-generated feedback and recommendations.
• Continuously research and evaluate new machine learning techniques and technologies to improve the platform’s performance and capabilities.
• Participate in code reviews, testing, and deployment processes.