Binance - Binance Accelerator Program

• Deploy and manage AI evaluation processes across our infrastructure to ensure reliable performance monitoring and continuous improvement of AI models. • Process and perform comprehensive data analysis to identify trends, inconsistencies, and opportunities for model enhancement. • Build and maintain scalable data pipelines that ingest raw data, perform transformations, and feed downstream systems. • Collaborate with cross-functional teams, including security, engineering, and product personnel, to embed security and compliance considerations into the development lifecycle. • Automate repetitive workflows and implement solutions to improve operational efficiency • Proficient in Python, with hands-on experience in making API calls using libraries such as requests and handling data formats like json. You will be expected to develop, test, and optimize scripts that interface with various data services and internal tools efficiently. • Strong data processing skills in PySpark and SQL (Hive), enabling you to extract, transform, and load large data workloads in distributed environments to support timely and accurate data insights. • Solid foundation in software development fundamentals, including version control (Git) • Excellent problem-solving abilities and a logical mindset, allowing you to analyze challenges critically, identify root causes, and devise effective solutions. • Eagerness to learn and strong communication skills, empowering you to engage effectively with team members, share knowledge, and take initiative in owning tasks from conception to completion, adapting quickly to new tools and technologies as needed. • Experience with AI or evaluation-related projects, whether personal, academic, or professional, demonstrating familiarity with AI concepts, data labeling, model performance assessment, or related methodologies. • Understanding of large language models (LLM) and issues such as hallucination, providing context for your work in AI evaluation and helping improve model reliability and trustworthiness. • Hands-on experience with tools such as Label Studio (for data annotation), Airflow (for workflow orchestration)