Time Series Data Analysis Based on Large Language Model (LLM) Engineer

With the major breakthroughs of large language models (LLMs) like BERT and GPT in the field of natural language processing, applying LLMs to time series (TS) tasks presents a promising direction. We are seeking an exceptional software engineer to focus on innovative work in this cutting-edge area. This role will explore and develop applications of LLMs in time series data analysis, forecasting, and anomaly detection.

Key Responsibilities

1. Research on LLMs for Time Series Tasks

• Conduct indepth research on the feasibility of applying LLMs such as BERT and GPT to time series analysis, forecasting, classification, anomaly detection, and other tasks.
• Design and implement novel models and algorithms to improve the performance and accuracy of LLMs in time series tasks.
• Explore and develop time series application scenarios in specific industries, such as data communication.

2. Model Development and Optimization

• Adjust and optimize existing LLMs to effectively handle and learn from time series data.
• Lead model training, finetuning, and performance optimization to address challenges posed by large-scale time series data.

3. Data Analysis and Applications

• Utilize time series databases (TSDB) and largescale time series data for data cleaning, feature engineering, and modeling.
• Explore time series data mining based on LLMs, combining datadriven methods and model predictions to enhance decision-making quality.

4. Cross-team Collaboration:

• Collaborate closely with the TSDB team to translate research results into realworld applications.

Qualifications

• Masters or Ph.D. in Computer Science, Artificial Intelligence, Data Science, Statistics, or related fields.
• Strong theoretical foundation and practical experience in natural language processing (NLP), machine learning, and deep learning.
• Familiarity with the architecture and principles of GPT and other mainstream LLMs, with handson research or development experience.
• Deep understanding of time series data and its applications (e.g., forecasting, classification, anomaly detection); knowledge of TSDB technologies is a plus.
• Proficiency in Python, PyTorch, and other development tools, with strong skills in algorithm design and implementation.
• Strong research and problemsolving abilities, capable of conducting independent research in frontier areas.

Preferred Qualifications

• Published relevant research papers in toptier conferences or journals.
• Experience with time series data applications in industries such as finance, IoT, or healthcare.
• Participation in opensource projects or contributions to LLM-related technology code.

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