Senior AI Research Scientist, Explainable Artificial Intelligence

Merck • 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

• 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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees