Bayer | Hiring | Machine Learning Operations Engineer | Whippany, NJ | United States | BigDataKB.com | 7 Oct 2022

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.

Machine Learning Operations Engineer

YOUR TASKS AND RESPONSIBILITIES

The primary responsibilities of this role, Machine Learning Ops Eng, are to:

• Reports to Head of Applied Machine Learning;
• Responsible for following best practices when developing the Data pipeline and Machine Learning (ML) infrastructure, including the implementation of technologies such as Kubernetes, Docker, and others, to support the creation of a scalable machine learning training and inference service;
• You will have a voice in the room from day one, assisting the Product Manager and other ML engineers with the evaluation of technical feasibility to ensure the proof of concept (POCs) are built, delivered, and monitored continuously against our impact metrics;
• Focus on auditability, versioning, and data security while establishing best practices for model development and selecting the appropriate cloud and on premises infrastructure for production level machine learning model deployment;
• Occasionally leads project teams composed of cross functional individuals;
• The preferred location for this role is Whippany, NJ or Boston/Cambridge, MA, however, residence-based candidates within the Eastern Time Zone of the US will be considered. Relocation assistance may be offered.
• Visa sponsorship may be offered for this role.

WHO YOU ARE

Your success will be driven by your demonstration of our 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 with five years of relevant experience; OR
  • Master’s degree with three years of relevant experience; OR
  • Ph.D. with one year of relevant experience;
• Relevant experience in Machine Learning Operations (MLOps) or software engineering role with focus on creation of scalable data workflow to support ML model development;
• Extensive expertise in object oriented programming, cloud computing and database systems, publishing, and consuming APIs, and building containers and Kubernetes for ML system maintenance;
• Familiarity with one or more data oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.) in addition to a working knowledge of software testing, benchmarking, and CI/CD processes, as well as test driven development (TDD) and behavioral driven development (BDD);
• Highly self motivated individual with exceptional analytic abilities and scientific rigor who aspires to scientific excellence;
• Capable of presenting complex topics effectively and convincingly to project teams or decision making bodies through good communication skills, while handling high complexity and exhibiting leadership potential through excellent soft skills.

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;
• Experience and proficiency working with high dimensional biological datasets, including transcriptomics, or connected multi omics (transcriptomics/proteomics/imaging) datasets;
• Prior experience with algorithms for single cell RNA seq data aggregation and analysis.

#LI-US