Machine Learning Research Engineer

OccamzRazor is pioneering a machine learning-driven drug discovery platform to cure neurodegenerative disorders, starting with Parkinson’s. Our platform consists of i) an information extraction pipeline which processes structured and unstructured data into a unified biomedical graph called the Parkinsome, and ii) a graph learning pipeline which further processes the biomedical graph to identify novel drug targets and/or compounds from it. Our goal is to leverage our computational platform to eventually identify curative therapies for all unsolved diseases.

OccamzRazor is comprised of a small and extraordinary team of machine learning engineers, biomedical scientists, and computational biologists, with half of them holding PhDs in their respective fields. The team is supported by the leading names in machine learning and biology, including our lead investor Jeff Dean (head of Google AI) and our advisor Randy Schekman (Nobel laureate). Members of our team have published various components of our computational platform in the leading machine learning (NeurIPS) and natural language processing (ACL) conferences.


OccamzRazor currently has offices in New York City and San Francisco and is actively seeking highly-motivated engineers and ML researchers to join the OccamzRazor family. We are a distributed team and open to collaborating with people who would like to live and work remotely anywhere in the world.


The role

We are looking for machine learning research engineers who have a record of excellence in solving complex problems via machine learning, data science, and engineering, and an enthusiasm for applying such methods in the biomedical domain. New engineers work on problems as diverse as

  • graph representation learning,
  • transfer learning,
  • semi-supervised learning,
  • dimensionality reduction,
  • clustering,
  • visualization,
  • natural language processing.

This role will report directly to the CTO.


On a day-to-day basis, you will 

  • Develop, take ownership, and execute an empirical research and engineering agenda to enable computational drug discovery.
  • Collaborate with and influence an interdisciplinary team of machine learning research engineers, computational biologists, and software engineers.
  • Employ the right mix of empirical machine learning research, data analysis and visualization, and software engineering in your own work.
  • Contribute to an industrial-grade codebase.

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