At Bayer we’re visionaries, driven to solve the world’s toughest challenges and striving for a world where ,Health for all, Hunger for none’ is no longer a dream, but a real possibility. We’re doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining ‘impossible’. There are so many reasons to join us. If you’re hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there’s only one choice.

Applied Engineer,Machine Learning

PURPOSE

In the Applied Machine Learning Group, the Applied ML Engineer will be responsible for a hand-on role in accomplishing the vision to create capabilities for machine learning-based analysis of high-dimensional biological datasets, such as transcriptomics or connected multi-omics, to support target identification and validation, as well as compound mode of action characterization and hit prioritization.

This is an interesting role in which you would have the opportunity to learn as well as utilize your knowledge to have a meaningful impact on our patients where it matters.

An ideal applicant would be internally driven and motivated by urgency as well as the potential of modernizing the drug development pipeline, must have a hands-on experience with productionizing machine learning models, be willing to speak out, and provide insightful suggestions.

YOUR TASKS AND RESPONSIBILITIES

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

• 
  Design and implement scalable tools/services for machine learning training and inference while selecting the suitable cloud and on-premises infrastructure for production-level machine learning models;
• Play a hands-on role in building interpretable ML products while incorporating suggestions from wet-lab scientists;
• Collaborate with other MLEngineers, MLScientists, and Wet-Lab scientists who are focused on multi-omics Life Science Technologies and connected teams in the omics realm of Drug Discovery Sciences.

WHO YOU ARE

Your success will be driven by your demonstration of our life values, more specifically related to this position, Bayer seeks an incumbent who possesses the following:

REQUIRED QUALIFICATIONS

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  Bachelor's degree with at least three years of relevant experience or a Master's degree with at least one year of relevant experience or fresh 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 as programming skill;
• 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);
• Experience and proficiency working with high-dimensional biological datasets, including transcriptomics, or connected multi-omics (transcriptomics/proteomics/imaging) datasets.

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;
• Prior experience with algorithms for single-cell RNA-seq data aggregation and analysis would be preferred.