Senior ML Scientist: Foundation Models for Biomedicine

Merck; Cambridge (MA); Hybrid; USD 145,000 - 228,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) retirement plan

Paid holidays and vacation

Sick days

Job summary

Merck is seeking a Senior Machine Learning Scientist to develop and train large-scale Foundation Models for biology, using state-of-the-art AI/ML techniques on high-performance GPU clusters. You will collaborate with computational biologists, data scientists, engineers and researchers across the company.

The role emphasizes transformer models, diffusion methods, and representation learning, with opportunities to publish findings and contribute to therapeutics development.

Qualifications

• PhD required in a relevant STEM field.
• Expertise in modern AI/ML approaches and large transformer-based Foundation Models.
• Experience with pre-training large-scale, multi-modal models on multiple GPUs.
• Proficiency in Python and PyTorch, with strong collaboration skills.

Responsibilities

• Collaborate with cross-functional team to identify research questions and data requirements, and develop AI/ML solutions.
• Design, train, and implement transformer-based Foundation Models for Target and Biomarker discovery using large biological data.
• Stay updated with AI/ML advancements and apply them to improve methodologies and models.
• Publish research findings in conferences/journals and contribute to the scientific community.

Skills

Transformer models

Python

PyTorch

Large-scale training

Diffusion models

Education

PhD in Computer Science, Applied Math, Physics, Computational Biology, Biostatistics, Bioinformatics, Engineering, AI/ML, Genetics/Genomics, or related STEM field

Tools

PyTorch

Job description

Merck is seeking a Senior Machine Learning Scientist to develop and train large-scale Foundation Models for biology, using state-of-the-art AI/ML techniques on high-performance GPU clusters. You will collaborate with computational biologists, data scientists, engineers and researchers across the company.

The role emphasizes transformer models, diffusion methods, and representation learning, with opportunities to publish findings and contribute to therapeutics development.