Director, Data Science, Foundation Model AI

MSD•Cambridge, MA

1d•Hybrid

About The Position

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 Director of foundational AI, you will be responsible for leading a team of machine learning researchers and engineers, overseeing both large foundation model development and the development of bespoke methods for extracting insights from noisy 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, and a team of exceptional machine learning researchers. 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.

Requirements

PhD in Computer Science, Statistics, Physics, Engineering, Mathematics, Data Science, AI/Machine Learning, Computational Biology, Bioinformatics, Computational Biology, or related STEM field and 7+ years of full-time experience or an MS and 10+ years of experience.
Deep technical expertise in classical machine learning, including probabilistic models and causal analysis.
Demonstrated world-class expertise in at least one sub-area of machine learning or AI, as shown by publications in NeurIPS, ICML, ICLR, AISTATS, or equivalent venues, and/or open-source projects.
Experience leading teams of machine learning researchers and software engineers.
Experience training large models on multi-node, multi-GPU environments.
Experience designing novel architectures for multi-modal foundation models.
Experience in post-training foundation models, including familiarity with parameter-efficient fine-tuning, post-hoc interpretability, and preference optimization.
Strong proficiency in Python and awareness of software engineering best practices.
Experience with standard deep learning frameworks like the PyTorch ecosystem.
Excellent communication skills and ability to work collaboratively in a multi-disciplinary team.
Interest in life sciences problems and disease biology, and willing to learn from and teach others.

Nice To Haves

Experience training and working with large transformer-based models is strongly preferred.
Experience with modern generative modeling paradigms, such as diffusion modeling and flow matching.
Experience or interest in reinforcement learning (RL) and using RL for training reasoning models.
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.

Responsibilities

Collaborate with cross-functional teams to identify research questions of high impact.
Align and prioritize the group’s research and development work with our company business needs.
Articulate the group’s agenda and demonstrate delivered business value to diverse stakeholders.
Provide deep technical guidance.
Oversee the training of large multi-modal foundation models.
Work with data, including various omics, imaging, and text modalities.
Interpret and critically analyze results from machine learning and AI models.
Cultivate and champion a culture of AI excellence within DAGS and across our company
Coach and advance the team of machine learning researchers.
Stay current with AI, machine learning, and statistics.
Proactively propose and pilot promising research directions.
Publish research findings in relevant conferences and journals and actively contribute to the scientific community through knowledge sharing and collaborations.
Establish and nurture existing collaborations with academia and industry.