Sr. Manager, Mcicc

Merck Merck · Pharma · Shanghai, China

Seeking a Scientist, MCICC to drive innovation in AI-powered drug evaluation and development. This role focuses on building and optimizing advanced AI systems, including large language models (LLMs) and molecular generation and property prediction models. The ideal candidate will combine deep technical expertise with domain knowledge in pharmaceutical R&D, enabling rapid iteration and deployment of cutting-edge solutions.

What you'd actually do

  1. building and optimizing advanced AI systems, including large language models (LLMs) and molecular generation and property prediction models
  2. Deep understanding and experience of LLM model architecture, training, and application in literature mining and scientific insights generation, including deployment, post-training, reasoning, tool-use, alignment, agents, as well as reinforcement learning and fine-tuning.
  3. Deep understanding and experience of graph neural networks, generative models and other deep learning models for drug discovery applications, including structure prediction, molecule design, property prediction, etc.
  4. Iterate collaboratively with scientists and domain experts in biology and chemistry. Experience of integrating scientific insights and data into AI models.

Skills

Required

  • Computational Chemistry
  • Computational Biology
  • Computer Science
  • Machine Learning
  • LLM model architecture
  • training
  • application
  • deployment
  • post-training
  • reasoning
  • tool-use
  • alignment
  • agents
  • reinforcement learning
  • fine-tuning
  • graph neural networks
  • generative models
  • deep learning models
  • structure prediction
  • molecule design
  • property prediction
  • PyTorch
  • TensorFlow
  • JAX
  • NumPy
  • SciPy
  • Pandas
  • Academic Conferences
  • Bioanalytical Analysis
  • Bioanalytical Techniques
  • Biochemical Assays
  • Bioinformatics
  • Biomarker Development
  • Clinical Judgment
  • Computational Neuroscience
  • Creativity
  • Detail-Oriented
  • Drug Development
  • Drug Discovery Process
  • Ethical Compliance
  • Immunohistochemistry (IHC)
  • In Vivo Pharmacology
  • Language Models
  • Large Language Models (LLMs)
  • Leadership
  • Management Process
  • Mass Spectrometry Analysis
  • Molecular Biology

Nice to have

  • disease and target research
  • drug discovery and development

What the JD emphasized

  • PhD or Master with equivalent practical experience
  • Deep understanding and experience of LLM model architecture, training, and application
  • Deep understanding and experience of graph neural networks, generative models and other deep learning models

Other signals

  • LLM model architecture, training, and application
  • generative models and other deep learning models for drug discovery applications
  • deployment, post-training, reasoning, tool-use, alignment, agents