Deep expertise in probabilistic models and causal analysis
The role involves leading a team to develop foundational AI models that accelerate the discovery of new medicines and therapeutic strategies
Job Summary
The role involves leading a team to develop foundational AI models that accelerate the discovery of new medicines and therapeutic strategies.
Candidates will utilize high-performance GPU clusters to train large multi-modal foundation models on diverse biological data including omics and imaging.
The position offers a comprehensive benefits package including medical, dental, vision, 401(k), and eligibility for annual bonuses and long-term incentives.
Matching Summary
The role involves leading a team to develop foundational AI models that accelerate the discovery of new medicines and therapeutic strategies.
Salary
Base: $194,100.00 - $305,600.00; Bonus/Equity: Eligible for annual bonus and long-term incentive; Benefits: Medical, dental, vision, 401(k), paid holidays, vacation
Skills & Requirements
Must-have
PhD in Computer Science or related STEM field
7+ years full-time experience in ML/AI
Deep expertise in probabilistic models and causal analysis
Experience training large multi-modal foundation models
Proficiency in Python and PyTorch ecosystem
Leadership of machine learning researchers and engineers
World-class publications in NeurIPS, ICML, ICLR
Nice-to-have
Interest in life sciences problems and disease biology
Experience with transformer-based models
Familiarity with diffusion modeling and flow matching
Experience with reinforcement learning for reasoning
Prior work with biological data and genomics
Collaboration with academia and industry partners
Key Requirements
PhD in CS, Statistics, Physics, Engineering, Math, Data Science, AI/ML, Computational Biology, or Bioinformatics
7+ years full-time experience (or MS + 10+ years)
Demonstrated world-class expertise via top-tier conference publications
Experience designing novel architectures for multi-modal foundation models
Experience with post-training techniques like parameter-efficient fine-tuning
Strong proficiency in software engineering best practices