Algorithm Engineer Intern (Fall 2026)

• Design, develop, and optimize machine learning and algorithmic solutions to address business challenges and improve user experience.
• Build and enhance recommendation, ranking, prediction, classification, or risk detection models across various business scenarios.
• Analyze large-scale datasets to identify patterns, generate insights, and improve algorithm performance.
• Develop and optimize data processing pipelines, feature engineering workflows, and model serving systems.
• Translate business requirements into scalable algorithmic and data-driven solutions.
• Evaluate, monitor, and improve model performance, accuracy, efficiency, and scalability.
• Collaborate with product, engineering, and cross-functional teams to deliver impactful solutions.
• Research and apply state-of-the-art techniques in machine learning, deep learning, large language models (LLMs), recommendation systems, information retrieval, anomaly detection, and related fields.
• Stay up to date with industry trends and emerging technologies, and contribute innovative ideas to business applications.

• Bachelor's degree or above in Computer Science, Artificial Intelligence, Data Science, Mathematics, Statistics, Engineering, or a related field.
• Strong programming skills in at least one language such as Python, C++, Java, or Go.
• Solid understanding of data structures, algorithms, probability, statistics, and machine learning fundamentals.
• Familiarity with data analysis and data processing techniques.
• Experience with or knowledge of machine learning and deep learning frameworks such as PyTorch, TensorFlow, or similar tools is preferred.
• Strong analytical thinking and problem-solving skills with a data-driven mindset.
• Good communication and collaboration skills, with the ability to work effectively in a team environment.
• Passion for applying algorithms and data science techniques to solve real-world problems.
• Self-motivated, detail-oriented, and eager to learn new technologies.

Good to have:

• Experience with recommendation systems, search and ranking systems, risk control, anomaly detection, natural language processing (NLP), large language models (LLMs), or information retrieval.
• Familiarity with big data technologies such as Spark, Hadoop, SQL, or related data processing frameworks.
• Knowledge of distributed systems, model deployment, and large-scale machine learning systems.