Responsibilities
1. Business Application: Responsible for applying self-developed algorithm models to enterprise collaboration software, creating the best AI tools and products in many office scenarios such as meetings, documents, messages, and office intelligence, and continuously improving user experience 2. Model Optimization: Responsible for training large language models to continuously improve the quality of their algorithms in the office field building efficient evaluation methods and technical systems collecting, investigating and producing high-quality data sets in the office field 3. Technology construction: continue to pay attention to new technology trends and research results in the industry, share industry best practices, and apply cutting-edge technologies to large models.
Qualifications
1. Have relevant experience in NLP, LLM application or RAG, or one of them 2. Strong programming ability, proficient in Python and other programming languages have a good understanding of AI-related Python libraries, such as Pandas 3. Strong innovation ability, with a strong desire and initiative to continuously explore new AI technologies and application scenarios 4. Keep up with new developments in the field of AI, and continuously improve your professional knowledge and skills by participating in academic conferences, reading cutting-edge papers, and taking online learning courses 5. Strong problem-solving ability, good at solving various tedious technical problems such as data quality problems, training problems, deployment compatibility problems, etc. sensitive to data, and detailed data work, including data quality, data distribution, case analysis, data tuning, etc. Bonus points: 1) Familiar with the concepts of traditional machine learning, and familiar with the definitions, goals, problems to be solved, and measurement indicators of various machine learning tasks 2) Familiar with deep learning frameworks, such as TensorFlow, PyTorch, etc. can use these frameworks to quickly build and train models to solve practical problems, such as using PyTorch to build a text classification model in natural language processing 3) Have a good mathematical foundation, understand probability theory, linear algebra, and calculus, be able to analyze the performance and error of the model through mathematical methods, and be able to understand learning tasks defined and expressed in formulas.
