Senior Machine Learning Research Scientist (m/f/d) - Generative AI for Drug Design

Pfizer Pfizer · Pharma · Berlin, Germany

Senior Machine Learning Research Scientist focused on Generative AI for Drug Design at Pfizer. The role involves designing, developing, and validating state-of-the-art machine learning models, particularly generative and self-supervised learning approaches, for molecular design challenges. It also includes developing predictive models and exploring novel representation learning techniques using large-scale datasets, with a focus on translating research into impactful applications in drug discovery.

What you'd actually do

  1. Design, develop, and validate state-of-the-art machine learning models, with a focus on generative AI and self-supervised learning
  2. Apply modern generative frameworks (e.g., diffusion or flow-based approaches) to molecular design challenges
  3. Develop predictive models combining structural and biochemical data (e.g., binding affinity prediction)
  4. Explore and implement novel representation learning approaches using large-scale, unlabeled datasets
  5. Translate emerging research in machine learning into impactful applications in drug discovery

Skills

Required

  • Advanced degree or equivalent experience in Computer Science, Machine Learning, Mathematics, Computational Biology, or a related field
  • Proven experience in developing machine learning models and algorithms
  • Strong programming skills (e.g., Python)
  • Experience working with scientific or complex structured datasets

Nice to have

  • Strong publication record in machine learning or computational science (e.g., NeurIPS, ICML, ICLR or comparable venues)
  • Hands-on experience implementing deep learning models using frameworks such as PyTorch
  • Expertise in modern generative modeling techniques, such as diffusion models, flow-matching approaches, reinforcement learning and/or self-supervised learning methods (e.g., JEPA)
  • Experience working with scientific data types relevant to drug discovery (e.g., molecular structures, protein data, or large-scale biological datasets)
  • Experience with high-performance computing environments (e.g., SLURM) and/or cloud platforms (e.g., AWS, Google Cloud)
  • Familiarity with cheminformatics tools (e.g., RDKit)
  • Proven ability to translate research ideas into applied solutions in a scientific or industrial setting

What the JD emphasized

  • proven experience in developing machine learning models and algorithms
  • strong programming skills (e.g., Python)
  • experience working with scientific or complex structured datasets
  • strong publication record in machine learning or computational science (e.g., NeurIPS, ICML, ICLR or comparable venues)
  • hands-on experience implementing deep learning models using frameworks such as PyTorch
  • expertise in modern generative modeling techniques, such as diffusion models, flow-matching approaches, reinforcement learning and/or self-supervised learning methods (e.g., JEPA)
  • experience working with scientific data types relevant to drug discovery (e.g., molecular structures, protein data, or large-scale biological datasets)

Other signals

  • generative AI
  • drug design
  • molecular design
  • predictive models
  • representation learning