Senior Machine Learning Scientist, Foundation Model

Senior Machine Learning Scientist Our Artificial Intelligence and Machine Learning (AI/ML) capabilities are critical accelerators of our mission to invent new medicines that save and improve lives. Core to the Data, AI, and Genome Sciences (DAGS) function is an AI/ML-first approach to improving target and biomarker discovery, validation, and selection, and elucidating complex disease mechanisms. As Senior Machine Learning Scientist, you will be responsible for developing and training large scale Foundation Models based on biological data. Your work will advance our understanding of complex diseases and support the development of innovative therapeutic strategies. In this pursuit, you will have at your disposal high-performance clusters with several hundred state-of-the-art GPUs, access to biological, computational, and engineering experts from across Our Company and external vendors. You will be part of a broader, cross-functional team of computational biologists, data scientists, software engineers, and machine learning researchers who strive to identify therapeutic targets and biomarkers. Primary Responsibilities Collaborate with cross-functional team to identify research questions, data requirements, and develop appropriate, modern AI/ML solutions. Design and develop, train, and implement novel ML algorithms particularly transformer-based Foundation Models for Target and Biomarker discovery leveraging large scale biological data. Stay up to date with the latest advancements in modern AI/ML approaches and apply relevant advancements to improve existing methodologies and models. Publish research findings in relevant conferences and journals, and actively contribute to the scientific community through knowledge sharing and collaborations. Required Education, Experience, And Skills PhD in Computer Science, Applied Math, Physics, Computational Biology, Biostatistics, Bioinformatics, Engineering, AI/ML, Genetics/Genomics, or a related STEM field. Strong expertise and experience in modern AI/ML approaches, training and working with large transformer-based Foundation models, representation learning, diffusion models, and related methodologies. Experience with pre-training large-scale and/or multi-modal Foundation Models on multiple GPUs. Proficiency in programming languages such as Python, and experience with standard deep learning frameworks like the PyTorch ecosystem. Interest in life sciences problems and disease biology, and willing to learn from and teach others. Excellent communication skills and ability to work collaboratively in multi-disciplinary team. Preferred Skills and Experience Familiarity and prior experience with biological data and biological foundation models are strong pluses. Relevant publications in scientific journals and experience contributing to research communities, including NeurIPS, ICML, ICLR, etc. Required Skills: Algorithms, Applied Mathematics, Artificial Intelligence (AI), Biological Data Analysis, Computational Sciences, Data Science, Deep Learning, Foundation Models, Genomics, Machine Learning (ML), Machine Learning Algorithms, Programming Languages, Python (Programming Language), PyTorch, Representation Learning, Transformer Model Preferred Skills: Salary range for this role is $144,800.00 – $227,900.00. This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting. An employee's position within the salary range will be based on several factors including, but not limited to relevant education, qualifications, certifications, experience, skills, geographic location, government requirements, and business or organizational needs. The successful candidate will be eligible for annual bonus and long-term incentive, if applicable. We offer a comprehensive package of benefits. Available benefits include medical, dental, vision healthcare and other insurance benefits (for employee and family), retirement benefits, including 401(k), paid holidays, vacation, and compassionate and sick days. More information about benefits is available at https://jobs.merck.com/us/en/compensation-and-benefits. Tagged as: Life Sciences