Pfizer Machine Learning Research Scientist in Cambridge, Massachusetts
The Machine Learning (ML) group within Computational Sciences seeks exceptional researchers in the areas of machine learning (probabilistic models, deep learning, and reinforcement learning) and related fields.
We are looking for applicants who have a demonstrated research background in machine learning, a passion for independent research and technical problem-solving, and a proven ability to develop and implement ideas from research.
The group provides a dynamic and challenging environment for cutting-edge, multidisciplinary research, both theoretical and applied, including access to heterogeneous data sources and close links to top academic institutions around the world as well as internal partners and research units. Our mission is to develop and apply methods to solve difficult, yet tractable, problems in the discovery, design, and development of therapeutic agents. As a research scientist, you will have the flexibility to work on impactful, real-world problems in the life sciences including large-scale gene expression data to augmentation of libraries through de novo molecule generation. Researchers will primarily focus on the deep learning subfield of machine learning but should be broadly interested in methods capable of efficient and effective feature learning that can help drive and sharpen the research questions we study.
Fundamentally, our research goals allow us to collaborate closely with and-contribute uniquely to-many different project teams across the company.
Participate in cutting edge research in machine learning and machine learning applications in drug discovery, design, and development. Specific subfields of research may include: deep generative models, metric learning, probabilistic models, computer vision, approximate inference techniques, variational inference using neural network recognition models, and reinforcement learning.
Leverage the scale of Pfizer proprietary data, applications, and GPU-compute infrastructure to develop custom, scalable machine learning methods for challenging problems in the life sciences
Provide technical leadership, driving and shaping research directions in Medicinal Sciences and broader R&D
Clearly communicate the value and efficiency of new methods, technologies, and research in order to advance adoption and awareness throughout R&D
Work closely with other teams within Computational Sciences as well as internal partners to transfer methods and technologies to new intelligent software and systems
Independently design and execute research projects
Ph.D. in Computer Science, related technical field or equivalent practical experience.
At least one year relevant work experience.
Experience contributing to research communities and/or similar efforts, including presentations/publications at conferences or workshops (such as CVPR, ICCV, ECCV, NIPS, ICML, ICLR, UAI, AISTATS, et c.), citations, etc.
Programming experience in one or more of the following: Java, Scala, C/C++, Python
Experience with one or more of the following: Torch, TensorFlow, Theano, Caffe
Relevant work experience, including research experience working in industry or an academic lab
Domain experience in developing and applying machine learning methods to one of the following areas: biology, chemistry, physics
Experience with distributed systems and frameworks
Programming experience with GPUs
Strong publication record and/or portfolio of open-source software
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Eligible for Employee Referral Bonus
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