Job Location: Gaithersburg, MD
Job title: Associate Director, AI Scientist – Machine Learning for Biologics Engineering
Location:Barcelona (Spain), Toronto (Canada), Gothenburg (Sweden), Cambridge (UK), Gaithersburg (USA),
Make a more meaningful impact to patients’ lives around the globe
Here you’ll have the opportunity to make a meaningful difference to patients’ lives. With science at its heart, this is the place where breakthroughs born in the lab become transformative medicines – for the world’s most complex diseases. Answer unmet medical needs by pioneering the next wave of science, focusing on outcomes and shaping the patient ecosystem. With our ground-breaking pipeline, the outlook is bright. Be proud to be part of a place that has achieved so much, yet is still moving forward. There’s no better time to join our global, growing enterprise as we lead the way for healthcare and society
The Center for Artificial Intelligence (CAI) is a lab focused on applying machine learning research to the most challenging problems at AstraZeneca. We innovate together with our leading biologists, chemists, and clinicians to close the gap between today’s challenges and the forefront of machine learning.
If you would like to have an impact on transforming patients’ lives by accelerating new medicines to patients, this may be the position for you!
What you’ll do
You will be part of an interdisciplinary team (in partnership with the Biologics Engineering) that is responsible for the discovery and optimization of next generation biological drug candidates for all the key therapy areas across AstraZeneca.
You will be working on the design and development of a cyclic discovery process for biologics engineering based on active learning/optimization/search (machine learning models inform the design of wet-lab experiments, the wet-lab automation generates new high-throughput data that is used for model re-training and update of the hypothesis informing the next design step), as well as development of deep learning algorithms for virtual screening of antibodies (supporting the efforts for in silico lead identification and de novo design of antibodies).
You will design, implement, test, and analyze machine learning algorithms to help contribute to the overall improvement and automation of the pipeline for biologics engineering. You will be required to interact extensively with other teams across the organization, and our academic partners with the goal of delivering products in a timely manner.
It is expected that you will present to various partners, represent us at conferences, and publish your findings in scientific journals or top conferences such as ICML, ICLR, NeurIPS, etc.
Accountabilities
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Work efficiently in a team of 3-10 (led by a Senior AI Researcher) to optimally deliver projects using the latest ML methods and modern engineering standard processes.
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Work multi-functionally with our partners from Biologics Engineering to develop machine learning methods for the design of antibodies with improved biological activity.
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Analyze challenges from the drug development process and provide creative solutions from the fields of representation learning, reinforcement learning, active learning, statistical learning theory, causality, ranking and recommendation.
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Remain at the forefront of ML Research by participating in journal clubs, mentoring, contributing publications and personal development projects.
Essential for the role
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PhD degree in computer science, statistics, applied mathematics, related field, or a completed MSc and at least three years of experience in developing machine learning models.
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Experience with active learning/optimization, graph neural networks, and large language models for proteins/antibodies.
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Expertise in developing machine learning methods for antibody design, lead optimization and identification.
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Knowledge of computational biology and demonstrated experience of incorporating it into machine learning models. Experience with deep learning methods and their applications to biology.
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ML Ops experience: model tracking and governance, multiple models in different production contexts, etc.
Desirable for the role
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A research program demonstrated by journal and conference publications in prestigious venues (with at least 1 publication as a leading author). Examples include but are not limited to: NeurIPS, ICML, ICLR, JMLR, etc.
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Deep understanding of drug development and clinical trial process and data (all phases).
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Track record of collaborating successfully with AI engineering teams to deliver complex machine learning models.
Why AstraZeneca?
At AstraZeneca we’re dedicated to being an excellent Place to Work. Where you are empowered to push the boundaries of science and unleash your ambitious spirit. There’s no better place to make a difference to medicine, patients and society. An inclusive culture that champions diversity and collaboration, and always committed to lifelong learning, growth and development. We’re on an exciting journey to pioneer the future of healthcare.
Why we love it
If your passion is science and you want to be part of a team that makes a bigger impact on patients’ lives, then there’s no better place to be. Here we truly understand science and apply it every day to strengthen and grow our pipeline.
So, what’s next?
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Are you already imagining yourself joining our team? Good, because we can’t wait to hear from you.
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Are you ready to bring new ideas and fresh thinking to the table? Brilliant! We have one seat available and we hope it’s yours.
Where can I find out more?
Our Social Media, Follow AstraZeneca on LinkedIn
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