Senior AI Research Scientist, Explainable Artificial Intelligence

MSD • Cambridge, MA

5d • Hybrid

About The Position

Our Artificial Intelligence Machine Learning (AI/ML) capabilities are critical accelerators to our mission of inventing 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 and elucidating complex disease mechanisms. As a senior AI scientist, you will be responsible for extracting biological insights from foundation models, developing new methods for post-hoc and intrinsic explainability of AI models, building rigorous benchmarks, evaluations, as well as fit-for-purpose datasets for answering specific biological questions. Your work will advance our understanding of complex diseases and support the development of innovative therapeutic strategies. You will be part of a cross-functional team of computational biologists, data scientists, software engineers, and machine learning researchers.

Requirements

• PhD, MS, or BS in Computer Science, Statistics, Physics, Engineering, Mathematics, Data Science, AI/Machine Learning, Computational Biology, Bioinformatics, Computational Biology, or related STEM field and 0-3+ years of full-time experience (with PhD), 4+ years of experience (with MS), or 7+ years of experience (with BS).
• Demonstrated experience in using biological foundation models for solving classical bioinformatics tasks, extracting new biological insights, and critically assessing model outputs.
• Demonstrated expertise in classical machine learning as well as modern deep learning approaches with a particular emphasis on graph neural networks.
• Experience in fine-tuning, evaluating, and debugging modern AI models and data at scale.
• Excellent software design and development skills and strong proficiency in Python.
• Experience with standard deep learning frameworks like the PyTorch ecosystem for working with large foundation models.
• Excellent communication skills and ability to work collaboratively in a multi-disciplinary team.

Nice To Haves

• Experience working with models that require multiple GPUs for inference.
• Relevant publications in scientific journals and experience contributing to research communities, including NeurIPS, ICML, ICLR, etc
• Experience with multi-modal (biological or otherwise) foundation models is a strong plus.

Responsibilities

• Collaborate with cross-functional teams to identify research questions and data requirements and develop appropriate solutions.
• Develop methods for extracting insights from large transformer-based (and related state-space models) foundation models for -omics data.
• Establish rigorous benchmarks and evaluation tasks for assessing the performance of AI models.
• Stay up to date with the latest advancements in machine learning and statistics 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.

Benefits

• 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree