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Applied Machine Learning Engineer 150 views

Applied Machine Learning Engineer

Your Tasks And Responsibilities

The primary responsibilities of this role, Machine Learning Operations Engineer, are to:

  • Reports to Head of Applied Machine Learning
  • Designs and implements scalable tools/services for machine learning training and inference while selecting the suitable cloud and on-premises infrastructure for production-level machine learning models.
  • Experience and proficiency working with high-dimensional biological datasets, including transcriptomics, or connected multi-omics (transcriptomics/proteomics/imaging) datasets.
  • Plays a hands-on role in building interpretable ML products while incorporating suggestions from wet-lab scientists
  • The primary location for this role is Cambridge, MA. Relocation assistance may be provided.
  • Visa sponsorship may be provided for this role.

Who You Are

Your success will be driven by your demonstration of the LIFE (Leadership Integrity Flexibility Efficiency) values. More specifically related to this position, Bayer seeks an incumbent who possesses the following:Required Qualifications:

  • Must have one of the following:
    • Bachelor’s degree in a relevant discipline with at least 3+ years of relevant experience, OR
    • Master’s degree in a relevant discipline with 1+ years of relevant experience, OR
    • Ph.D. in Machine Learning with an emphasis on drug discovery preferred.
  • Demonstrated experience with open-source deep learning frameworks, such as TensorFlow/PyTorch, as well as competency in Python programming;
  • Direct knowledge regarding the implementation of deep learning techniques including Transformers, CNNs, and VAE;
  • Highly self-motivated individual with exceptional analytic abilities and scientific rigor who aspires to scientific excellence;
  • Capable of presenting their work in a simplified manner to effectively communicate with other senior members to arrive at a decision about the progress of the project(s);

Preferred Qualifications:

  • Familiarity with concepts and actors in molecular biology, such as the principles of gene regulation and the interactions and connections between genes, RNA, and proteins;
  • Basic understanding of outputs of omics technologies, particularly transcriptomics and gene expression determination, ideally with an understanding of the analysis of RNA-seq data;
  • Record of Publications.

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