Senior AI Research Scientist (6240)

TSMC•San Jose, CA

14d•$170,000 - $270,000•Hybrid

About The Position

Taiwan Semiconductor Manufacturing Company (TSMC) is seeking applications for skilled Senior Artificial Intelligence (AI) Research Scientists for their Artificial Intelligence for Business Intelligence (AI4BI) Center. The AI4BI Center is a global and international team that seeks to develop advanced AI-enabled analytics techniques to facilitate important business intelligence applications highly relevant to TSMC. This role will be based in our San Jose office in a hybrid working environment (working four days in the office). Individuals in this position may be responsible for tasks across two technical areas. The first is designing and implementing significant machine learning, deep learning, text mining, Large Language Model (LLM), foundation model, and/or network science-based approaches to extract insights from structured (e.g., transactional) and unstructured data sources (e.g., 10K reports, social media posts, news) for various BI applications. Second, the position may require efforts in developing time series modelling and/or reinforcement learning-based approaches for analyzing fab tool productivity and facilitating effective wafer start strategies. This role requires close interaction and coordination with international teams of junior AI research scientists, data engineers, and business stakeholders to gather requirements, oversee development, evaluate the outputs of technical solutions, produce internal reports, and lead co-authored publications at top-tier AI venues.

Requirements

Ph.D. in computer science, information systems, information science, statistics, or related AI or machine learning field
At least 7-10 years of significant AI, machine learning, and data science-related working and research experience
Experience gathering requirements from business stakeholders and developing technical machine learning designs for implementation
Strong practical experience with data wrangling, pre-processing, and extraction from structured and unstructured data sources
Strong conceptual and practical knowledge and experience of classical machine learning algorithms and learning paradigms, including supervised learning and unsupervised learning
Strong skills in deep learning implementation, including data encodings, processing units, and learning paradigms
Proficiency in developing novel machine learning or deep learning algorithms based on unique dataset characteristics and business requirements
Strong practical network science skills and experience
Experience with text analytics for named entity recognition, sentiment analysis, and topic modelling.
Hands-on research experience with time series modeling and forecasting with statistical, machine learning, deep learning-based, and/or foundation model (TimesFM) approaches
Hands-on research experience with reinforcement learning approaches, including value-based, policy-based, actor-critic, and/or multi-objective
Hands-on research experience, including literature review, model design, implementation, evaluation, and publication
Experience fine-tuning LLMs and other foundation models with strategies such as low rank adaptation, few shot learning, and others
Experience with prompt engineering on LLMs and other foundation models with techniques such as reinforcement learning (e.g., Direct Preference Optimization (DPO), Proximal Policy Optimization (PPO)), prompt tuning, and others
Knowledge about deploying models into user interface tools for dashboarding (e.g., PowerBI), Python frameworks for rapid prototyping and interaction (e.g., Streamlit, Dash), and/or JavaScript frameworks (e.g., React)
Must be willing to travel to Taiwan for at least 1-3 months each year for training, team building, and project coordination

Nice To Haves

Knowledge on processing text in financial, accounting, and market analysis reports
Multi-lingual text analysis using packages like Stanza, Polyglot, or Textflint.
Knowledge of graph embedding techniques such as graph convolutional networks, graph attention networks, and graph transformers using packages such as stellargraph, PyG, or Deep Graph Library.
Understanding of neural information retrieval methods, including deep structured semantic models, entity resolution, and retrieval augmented generation (RAG).
Familiarity and practical experience with learning paradigms such as adversarial learning, knowledge distillation, and self-supervised learning.
Experience working with foundation models for language (e.g., LLaMA), time (e.g., TimesFM), and other data modalities
Proven and extensive track record publishing in leading AI conferences (e.g., NeurIPS, ICLR, ICML, KDD) and/or journals (e.g., IEEE TKDE, ACM TOIS)

Responsibilities

Create and manage advanced machine learning, deep learning, LLM, foundation models, time-series modelling, and/or reinforcement learning algorithms to develop data-driven solutions for complex business problems
Analyze and interpret complicated data sets to provide actionable insights that aid in enhancing decision-making processes for business stakeholders
Work with cross-functional and international teams to identify and prioritize data-driven opportunities, including the development and deployment of predictive models to drive business outcomes
Lead teams of junior data scientists and data engineers to develop efficient procedures, follow best practices, and provide implementation guidance (e.g., code reviews, demos) for gathering, retaining, and scrutinizing data
Consistently assess and refine model performance to ensure precision and dependability
Communicate insights and recommendations via internal reports to technical and non-technical stakeholders
Design and implement the latest AI, machine learning, and data science tools and techniques to enhance existing processes
Produce publications in leading AI conference and/or journal venues

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