Senior AI Scientist, Foundation Models

Merck-posted 11 days ago
Full-time • Mid Level
Hybrid • Cambridge, MA
5,001-10,000 employees
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AI Research Scientist, Foundation Models 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, validation and selection, and elucidating complex disease mechanisms. As a senior AI scientist, you will be responsible for pre-training and fine-tuning biological foundation models on text, multi-omics, and imaging data, analyzing pre-trained models posthoc, building rigorous benchmarks for evaluating foundation models, and serving in-house trained foundation models. 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, bioinformaticians, data scientists, software engineers, and machine learning researchers who strive to identify therapeutic targets and biomarkers.

  • Collaborate with cross-functional teams to identify research questions and data requirements and develop appropriate solutions.
  • Develop and train large foundation models for -omics data augmented with text and images.
  • Interpret and critically post-hoc analyze pre-trained models.
  • Rigorously benchmark and evaluate the performance of both in-house and publicly available models.
  • Host and serve in-house state-of-the-art models and make them accessible to scientists across our company.
  • 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.
  • PhD, MS, or BS in Computer Science, Engineering, Data Science, AI/ML, Bioinformatics, Computational Biology, Genetics & Genomics, Mathematics, Statistics, Physics, Pharmaceutical Science, or related STEM field and 0+ years of full-time experience (with PhD), 4+ years of experience (with MS), or 7+ years of experience (with BS).
  • Experience training large models on multi-node, multi-GPU environments.
  • Experience designing novel architectures for multi-modal foundation models.
  • Deep expertise in post-training foundation models, including some parameter efficient fine-tuning, post-hoc interpretability, and preference optimization.
  • Demonstrated expertise in classical machine learning, statistical models, and in training, evaluating, and debugging 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.
  • Interest in life sciences problems and disease biology, and willing to learn from and teach others.
  • Experience with pre-training biological foundation models (transcriptomic, protein language models, DNA transformers, or structure-to-function models) is a strong plus.
  • Familiarity with biological data and previous experience with protein language models and foundation models for omics is a strong plus.
  • Experience training and working with large discrete diffusion models.
  • Experience with reinforcement learning (RL) and using RL for training reasoning models.
  • Relevant publications in scientific journals and experience contributing to research communities, including NeurIPS, ICML, ICLR, etc.
  • 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.
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