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 Py. Torch, Tensor. Flow, 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.