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

A career at Pfizer offers opportunity, ownership and impact.
All over the world, Pfizer colleagues work together to positively impact health for everyone, everywhere. Our colleagues have the opportunity to grow and develop a career that offers both individual and company success; be part of an ownership culture that values diversity and where all colleagues are energized and engaged; and the ability to impact the health and lives of millions of people. Pfizer, a global leader in the biopharmaceutical industry, is continuously seeking top talent who are inspired by our purpose to innovate to bring therapies to patients that significantly improve their lives.
Right now, we are seeking highly qualified candidates to fill the position:
Senior Machine Learning Research Scientist (m/f/d) - Generative AI for Drug Design
Location: Berlin, Germany
Join our pioneering team at the forefront of AI-driven drug discovery.
As authors of the FLOWR and PILOT frameworks, we are expanding our group of machine learning research scientists to further advance the development and application of state-of-the-art generative models for both structure- and ligand-based drug design. In this role, you will design, implement, and validate novel machine learning tools that generate testable hypotheses and help accelerate the entire drug discovery continuum. You will work with Pfizer's rich proprietary data, large-scale external datasets, and ultra-large data from strategic collaborations to push the boundaries of our machine learning capabilities. This is a unique opportunity to contribute to cutting-edge research while translating innovation into real-world impact for patients.
What You Will Do

• Design, develop, and validate state-of-the-art machine learning models, with a focus on generative AI and self-supervised learning
• Apply modern generative frameworks (e.g., diffusion or flow-based approaches) to molecular design challenges
• Develop predictive models combining structural and biochemical data (e.g., binding affinity prediction)
• Explore and implement novel representation learning approaches using large-scale, unlabeled datasets
• Translate emerging research in machine learning into impactful applications in drug discovery
• Collaborate with cross-functional experts in computational biology, chemistry, and medicine design
• Contribute to publications, conferences, and external scientific engagement

Your Profile
We are looking for individuals with strong technical expertise and curiosity to drive innovation. You may bring experience through different pathways:
Required Qualifications:

• 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

Preferred Qualifications:

• 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

Technologies We Use
Slurm-based on-premise compute clusters, Google Cloud Platform, AWS, Docker, Kubernetes, Python (PyTorch, numpy, pandas, scikit-learn, RDKit).
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