Director, Data Science, Foundation Model, AI
Job Description
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.
Primary Responsibilities
- Collaborate with cross-functional teams to identify research questions of high impact
- Align and prioritize the group's research and development work with 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 the 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
- Establish and nurture existing collaborations with academia and industry
Required Education, Experience, and Skills:
- PhD in Computer Science, Statistics, Physics, Engineering, Mathematics, Data Science, AI/Machine Learning, Computational Biology, Bioinformatics, or related STEM field, and 7+ years of full-time experience (or MS with 10+ years)
- Deep technical expertise in classical machine learning, including probabilistic models and causal analysis
- Demonstrated world-class expertise in at least one sub-area of ML/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 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
Preferred Skills and Experience
- 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
- Relevant publications in scientific journals and contributions to research communities (NeurIPS, ICML, ICLR, etc.)
Compensation & Benefits
Salary range: $194,100 - $305,600. Eligible for annual bonus and long-term incentive. Comprehensive benefits including medical/dental/vision, 401(k), paid holidays, vacation, and sick days.
Work Arrangement: Hybrid (3 days/week on-site, Monday-Thursday)
Travel: 10%
Visa Sponsorship: Yes
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.
Primary Responsibilities
- Collaborate with cross-functional teams to identify research questions of high impact
- Align and prioritize the group's research and development work with 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 the 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
- Establish and nurture existing collaborations with academia and industry
Required Education, Experience, and Skills:
- PhD in Computer Science, Statistics, Physics, Engineering, Mathematics, Data Science, AI/Machine Learning, Computational Biology, Bioinformatics, or related STEM field, and 7+ years of full-time experience (or MS with 10+ years)
- Deep technical expertise in classical machine learning, including probabilistic models and causal analysis
- Demonstrated world-class expertise in at least one sub-area of ML/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 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
Preferred Skills and Experience
- 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
- Relevant publications in scientific journals and contributions to research communities (NeurIPS, ICML, ICLR, etc.)
Compensation & Benefits
Salary range: $194,100 - $305,600. Eligible for annual bonus and long-term incentive. Comprehensive benefits including medical/dental/vision, 401(k), paid holidays, vacation, and sick days.
Work Arrangement: Hybrid (3 days/week on-site, Monday-Thursday)
Travel: 10%
Visa Sponsorship: Yes
Relocation: Domestic
Requisition ID: R392839
Get Similar Jobs in Your Inbox
Weekly digest of top bioinformatics jobs. No spam.
Job Information
Get Similar Jobs by Email
Weekly digest of Merck and similar companies. Free.