Algorithm Engineer Intern (Fall 2026)

Job Description:

* 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

Candidate Requirements:

* 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