Job Description
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Company Overview
A leading global healthcare and life sciences organization is advancing drug discovery and development through artificial intelligence, data science, and computational technologies. Its research organizations are leveraging advanced computational models, biological data, and AI to accelerate research and develop innovative medicines for patients worldwide.
The organization has established a unified Computational Sciences Center of Excellence to harness the transformative potential of data and artificial intelligence across research and early development. The initiative focuses on improving access to data and computational models while enabling scientists across research disciplines to apply advanced technologies to complex biomedical challenges.
The AI Biology & Translation (AIBT) team develops and applies state-of-the-art artificial intelligence to accelerate biomedical discovery. The team combines foundation models, multimodal machine learning, and large-scale biological datasets to support target discovery, disease understanding, biomarker development, and translational research.
Key Responsibilities
- Develop and apply state-of-the-art machine learning methods to solve complex problems in biology and translational research.
- Develop, adapt, and evaluate foundation models, large language models (LLMs), multimodal models, and generative AI approaches for biomedical applications.
- Design scalable machine learning workflows that integrate diverse biological, molecular, imaging, and clinical datasets.
- Lead technical efforts to optimize, benchmark, validate, and interpret AI models for scientific applications.
- Collaborate with computational scientists, software engineers, biologists, clinicians, and therapeutic area researchers to translate advanced AI technologies into impactful scientific capabilities.
- Establish and promote best practices for model development, evaluation, reproducibility, and deployment.
- Drive technical excellence across machine learning initiatives and scientific applications.
- Monitor emerging developments in machine learning and identify opportunities to apply new AI technologies to biomedical discovery.
- Contribute to the advancement of AI-driven approaches for biology, translational science, and drug discovery.
Required Qualifications
- Ph.D. in Machine Learning, Computer Science, Artificial Intelligence, Computational Biology, Statistics, or a related quantitative discipline, combined with significant postdoctoral and/or industry experience.
- Demonstrated expertise in deep learning, foundation models, generative AI, large language models, multimodal learning, or related fields.
- Proven experience leading technically complex machine learning projects from research through deployment or scientific application.
- Strong programming skills in Python.
- Experience with modern machine learning frameworks such as PyTorch, JAX, TensorFlow, and Hugging Face.
- Experience adapting, fine-tuning, evaluating, and deploying large-scale AI models.
- Experience developing reproducible and scalable machine learning pipelines and software.
- Excellent communication skills with the ability to collaborate effectively across multidisciplinary scientific teams.
- Strong interest in applying advanced AI technologies to biology, translational science, and drug discovery.
Preferred Qualifications
- Publications or significant technical contributions in machine learning, artificial intelligence, computational biology, or related disciplines.
- Experience working with multimodal biomedical datasets, including genomics, transcriptomics, proteomics, imaging, pathology, or clinical data.
- Experience building production-quality AI systems.
- Experience developing scalable machine learning infrastructure.
- Experience translating advanced machine learning research into practical scientific applications.
- Demonstrated ability to work effectively across computational, experimental, and clinical disciplines.
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