Scientific Developer Intern – Machine Learning

At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.

OpenEye, Cadence Molecular Sciences - a division of Cadence Design Systems - is an industry leader in computational molecular design through rapid, robust, and scalable software, consulting services, and Orion®, the only cloud-native fully integrated software-as-a-service molecular modeling platform. Combining unlimited computation and storage with powerful tools for data sharing, visualization and analysis in a customizable development platform, Orion offers unprecedented capabilities for the advancement of pharmaceuticals, biologics, agrochemicals, and flavors and fragrances. OpenEye, Cadence Molecular Sciences is headquartered in Santa Fe, N.M., with offices in Boston, Mass.; Cologne, Germany; and Tokyo, Japan

In recent years OpenEye has expanded its structural biology expertise. As a Scientific Developer Intern, you will have the opportunity to work with PhD-level scientists in the rapidly developing field of computational CryoEM. In particular, you will play an active part in novel machine learning research relevant to CryoEM imaging data. You will collaborate in an interdisciplinary way with OpenEye colleagues having varying backgrounds in the life sciences as well as computer science.

Key Learnings:

• Experience working on an exploratory research problem in CryoEM-based computational imaging.
• Experience handling and analyzing data with large amounts of noise.
• Experience developing scientific software in a professional setting.

Education:

At least an undergrad degree in Computer Science or a computationally focused field with experience in intermediate to advanced algorithm development.

What you should have (formally MUST HAVES/REQUIREMENTS):

• Intermediate Python programming skills
• Working knowledge of standard concepts in clustering analysis.
• Experience with algorithm validation and evaluation.
• Some experience with foundational neural net architectures (e.g. convolutional neural networks).
• An introductory background in basic concepts of probability theory.

The following are a PLUS, but not required (preferred skills/experience):

• Advanced Python programming skills
• Intermediate to advanced experience with the Pytorch or Tensorflow deep learning frameworks.

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