Senior AI Research Scientist, Explainable Artificial Intelligence

Job Description

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.

Primary 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.
   

Required Education, Experience and Skills

• 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.

Preferred Skills and Experience

• 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.

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Required Skills:

Artificial Intelligence (AI), Data Engineering, Data Science, Deep Learning, Foundation Model Fine Tuning, Foundation Models, Genomics, Machine Learning (ML), Python (Programming Language), PyTorch, Software Development, Transformer Model

Preferred Skills:

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