Associate Principal AI Engineer
AstraZeneca AB / Datajobb / Göteborg
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hela Sverige Join the Center for Artificial Intelligence (CAI) at AstraZeneca, a lab dedicated to applying machine learning research to the toughest challenges in the medical field. We are a diverse team of experts from various fields including Machine Intelligence, Data Science, Real World Evidence and Bioinformatics.
As an Associate Principal AI Engineer, you will help deliver innovative AI research as elegant, enterprise-ready products. You will be responsible for developing efficient and scalable processes for the distributed training of large machine learning (ML) models, as well as automating our processes to accelerate project delivery and reduce costs and environmental impact. The role is to take the most important research done at the CAI and deliver it as elegantly engineered fully compliant, enterprise ready products. This role is based at our vibrant R&D site in Gothenburg, Sweden.
Key Accountabilities
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Take technical leadership of projects regarding software design, reproducibility and efficiency.
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Ownership of software engineering standards within the team, the documentation of these standards along with the development of artifacts that make it simple to apply to new or existing projects.
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Developing efficient and scalable processes for the distributed training of large machine learning models in both cloud and on-prem infrastructure. This will involve working with both internal infrastructure teams and external vendors and research institutes.
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Accelerating project delivery, driving down cost and environmental impacts of the CAI by DevOps, GitOps and MLOps automation, thereby reducing the need for human intervention in our processes.
Essential Skills and Experience
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A master's degree or PhD in a relevant field.
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A track-record of implementing software engineering best practices for multiple use cases.
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Professional experience automating the entire machine learning model lifecycle.
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Advanced proficiency in Python and common scientific libraries (e.g. Numpy, Pandas).
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Experience using PyTorch to implement machine learning models and training.
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Demonstratable experience with optimization of distributed training of machine learning models.
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Experience with GitHub for source control and GitHub Actions for CI/CD.
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Experience using Kubernetes to implement machine learning pipelines and the Kubeflow ecosystem.
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Experience with deployment of solutions on a cloud vendor such as AWS, GCP or Azure.
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Experience partnering and/or collaborating with teams that have different competences.
Desirable Skills and Experience
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Relevant certifications.
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Experience working in a domain subject to regulatory oversight.
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Experience working in scientific research environment.
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Understanding of the pharmaceutical industry and its processes.
Why AstraZeneca
At AstraZeneca, we follow the science to explore and innovate. We are working towards treating, preventing, modifying and even curing some of the world's most complex diseases. Here we have the potential to grow our pipeline and positively impact the lives of billions of patients around the world. We are committed to making a difference. We have built our business around our passion for science. Now we are fusing data and technology with the latest scientific innovations to achieve the next wave of breakthroughs.
So, what's next?
Are you ready to be part of a team where you are empowered to follow the science? Ready to make an impact? Apply now and join us in our mission to improve patient access to healthcare globally!
We welcome your application no later than June 19th, 2024. Applications will be reviewed continuously and interviews may be held on a continuous basis.
Ersättning Not Specified
Så ansöker du Sista dag att ansöka är 2024-06-08
Klicka på denna länk för att göra din ansökan Arbetsgivarens referens Arbetsgivarens referens för detta jobb är "R-195988".
Omfattning Detta är ett heltidsjobb.
Arbetsgivare Astrazeneca AB (org.nr 556011-7482)
Arbetsplats AstraZeneca
Kontakt AstraZeneca
karin.tomin@astrazeneca.com Jobbnummer 8713667
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