Machine Learning Lead

17 hours ago

Boston, Massachusetts, United States Vertex Pharmaceuticals Full time
Job Summary

Vertex Pharmaceuticals is seeking a highly skilled machine learning expert to lead machine learning efforts in the Data and Computational Sciences (DCS) team. As a Senior Machine Learning Research Fellow, you will be responsible for developing and applying machine learning and artificial intelligence across applications in global research, preclinical, and pharmaceutical sciences.

In this leadership role, you will lead the development and application of machine learning and artificial intelligence to improve, advance, and accelerate the discovery and development of transformative small molecule medicines, cell therapies, and genetic therapies.

You will have the opportunity to impact a variety of scientific areas, including predictive models for small molecule compounds, protein structure prediction, lipid nanoparticle design, and cell therapy characterization and manufacturing.

The initial focus of the role will be on small molecule drug discovery, including opportunities around predictive models and protein structure prediction.

You will work collaboratively with scientific and data science experts across Vertex to identify, evaluate, and implement modern methods for predictive models and generative models, and apply them to small molecule datasets.

You will also drive method development to support active learning approaches in molecular design, reaction screening, and potentially other areas where iterative optimization is applicable.

You will be a key member of the Data & Methods Leadership Team, which sets the strategy for our approach to foundational methods and capabilities for all of DCS.

You will also shape the future direction around use of Machine Learning and Artificial Intelligence for scientific applications, by evaluating opportunities across the global research, preclinical & pharmaceutical science projects at Vertex, and partnering with other data and technology leaders to define the infrastructure needed for those goals.

For success in this role, you will need to be an independent thinker with a strong sense of ownership, excellent communication skills, and the capability to drive research ideas from first principles-based conceptualization to realization.

In addition to having the ability to contribute to engineering solutions when required, you should be able to balance theoretical elegance of methods with practical considerations of implementation, to ensure that the methods are able to truly advance the discovery and development of transformative therapies.

Responsibilities
  • Develop and lead machine learning methods for key scientific questions across global preclinical research & pharmaceutical sciences
  • Partner with scientific experts to evaluate and develop machine learning methods in various scientific domains, with an initial focus on partnering with chemistry and computational chemistry leaders to accelerate and enhance the discovery of small molecule medicines through improvements to the Design-Make-Test-Analyze cycle
  • Lead data integrity efforts to define guidelines that ensure that ML/AI models have appropriate governance and documentation processes
  • Evaluate and benchmark machine-learning methods related to key scientific applications, such as small molecule property prediction and design, generative models and active learning
  • Modify existing methods, or implement novel methods to address project needs that are amenable to ML/AI approaches
  • Collaborate with software developers in Data Technology and Engineering and the ML/AI development group within DCS to transition novel methods into the production MLOps system
  • Drive strategic partnerships with other data science groups across the network
Qualifications
  • PhD/MS in computer science, statistics, or computational science, with a focus on machine learning methods, or a PhD in a related field (e.g., computational chemistry or biology) with a strong emphasis in machine learning method development, and 10+ years of relevant experience
  • Deep expertise in the mathematics & algorithms for machine learning, both classical as well as modern deep learning methods (transformers, RL, GNN), and active learning & model confidence methodologies
  • Excellent ability to strategically evaluate opportunities, communicate and collaborate with subject matter experts of diverse backgrounds, and rapidly learn about new scientific areas
  • Very strong Python programming skills and experience with modern machine learning tooling (scikit-learn, JAX, PyTorch, Tensorflow, etc.)
  • Experience in visualization and interpretation (xAI) of ML/AI models
  • Scientifically diverse, authoritative and effective communicator, with excellent verbal and written communication skills
  • Knowledge of concepts related to drug discovery and development

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