Senior Machine Learning Scientist, Foundational ML, AI for Biology & Translation (AIBT)

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Company Overview

A leading global healthcare and life sciences organization is advancing the use of artificial intelligence, data, and computational sciences to transform drug discovery and development. Its research organizations are leveraging advanced computational models and data-driven approaches to accelerate research and develop innovative medicines for patients worldwide.

Key Responsibilities

• Design and develop foundation models supporting target and drug discovery, with an emphasis on large-scale representation learning, multimodal generative models, LLMs, AI agents, and reinforcement learning.
• Work with and integrate diverse data modalities, including molecular structures, biological sequences, omics data, biochemical readouts, and scientific text.
• Bridge advanced AI models with applications supporting target discovery, experimental design, and laboratory-in-the-loop workflows.
• Scale frontier AI models across massive datasets while addressing deep learning and engineering challenges involving system design, architecture, and scalability.
• Collaborate with engineering and MLOps teams to develop scalable and reliable machine learning systems.
• Lead high-impact collaborative research projects across multidisciplinary teams.
• Publish research findings in leading machine learning venues and scientific journals.
• Present research and technical results at internal and external conferences, workshops, and scientific forums.
• Collaborate closely with interdisciplinary and cross-functional teams across research and development organizations.
• Develop and implement innovative machine learning research ideas that can translate into practical scientific applications.

Required Qualifications

• Ph.D. in Computer Science, Machine Learning, Computational Biology, or a related quantitative discipline.
• 0–2+ years of industry or postdoctoral experience.
• Proven track record of advancing machine learning models in research or industry environments.
• Experience with large-scale representation learning, multimodal generative models, LLMs, AI agents, and/or reinforcement learning.
• Demonstrated interest in applying AI to scientific challenges across biology, chemistry, and drug discovery.
• Excellent understanding of deep learning theory and practical implementation.
• Proven experience developing and delivering innovative machine learning solutions.
• Excellent Python programming skills.
• Experience with modern agentic coding environments.
• Extensive experience with machine learning frameworks such as PyTorch or JAX.
• Strong understanding of software engineering, data engineering, and MLOps best practices.
• Experience with version control, high-performance computing infrastructure, and machine learning experiment monitoring workflows.
• Strong research publication record and active participation in the machine learning research community.
• Excellent communication, collaboration, and problem-solving skills.

Preferred Qualifications

• Practical experience applying innovative machine learning methods to target or drug discovery.
• Experience working with biological and chemical data modalities.
• Knowledge of molecular structures, single-cell and omics data, perturbation biology, and multimodal biological datasets.
• Hands-on experience developing, fine-tuning, and optimizing large language models.
• Experience developing and working with agentic AI systems.
• Experience translating advanced machine learning research into practical scientific applications.
• Publications or research contributions to leading machine learning venues such as NeurIPS, ICML, ICLR, AAAI, ACL, EMNLP, or AISTATS.
• A public portfolio of relevant machine learning or scientific computing projects hosted on platforms such as GitHub or GitLab.

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