Associate Principal, AI Engineer

at  AstraZeneca

At AstraZeneca, we pride ourselves on crafting a collaborative culture that champions knowledge-sharing, ambitious thinking and innovation – ultimately providing employees with the opportunity to work across teams, functions and even the globe.
Recognizing the importance of individualized flexibility, our ways of working allow employees to balance personal and work commitments while ensuring we continue to create a strong culture of collaboration and teamwork by engaging face-to-face in our offices 3 days a week. Our head office and BlueSky Hub in downtown Toronto are purposely designed with collaboration in mind, providing space where teams can come together to strategize, brainstorm and connect on key projects.
Our dedication to sustainability is also central to our culture and part of what makes AstraZeneca a great place to work. We know the health of people, the planet and our business are interconnected which is why we’re taking ambitious action to tackle some of the biggest challenges of our time, from climate change to access to healthcare and disease prevention.
Introduction to role
Are you ready to take the most important research done at the Center for AI (CAI) and deliver it as elegantly engineered fully compliant, enterprise ready products? This is your chance to join a global, science-led, patient-focused biopharmaceutical company where scientific leadership changes patients’ lives. Help push the boundaries of science to deliver life-changing medicines to patients. With science at its heart, this is where breakthroughs born in the lab become transformative medicine – for the world’s most complex diseases.
Accountabilities
As an Associate Principal AI Engineer, you will take technical leadership of projects regarding software design, reproducibility and efficiency. You will be responsible for the 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. You will also be tasked with 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. Lastly, you will be 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/Experience:

• A master’s degree or PhD in a relevant field
• A track-record of implementing software engineering best practices for multiple use cases.
• Professional experience automating the entire machine learning model lifecycle.
• Advanced proficiency in Python and common scientific libraries (e.g. Numpy, Pandas).
• Experience using PyTorch to implement machine learning models and training.
• Demonstratable experience with optimization of distributed training of machine learning models.
• Experience with GitHub for source control and GitHub Actions for CI/CD.
• Experience using Kubernetes to implement machine learning pipelines and the Kubeflow ecosystem.
• Experience with deployment of solutions on a cloud vendor such as AWS, GCP or Azure.
• Experience partnering and/or collaborating with teams that have different competences.

Desirable Skills/Experience:

• Relevant certifications.
• Experience working in a domain subject to regulatory oversight.
• Experience working in scientific research environment.
• Understanding of the pharmaceutical industry and its processes.

Please refer the Job description for details