Machine Learning Scientist – Molecular Foundation Models

Are you a skilled machine learning scientist with experience in using the latest artificial intelligence methods to train models at scale Do you want to make a difference in how the next generation of drug candidates are designed Can you imagine yourself contributing to life changing science

We are looking for an early career researcher to join the Molecular AI Department within Discovery Sciences where your machine learning skills will play a key role in evolving our AI capacity for molecular design. The role is located at our vibrant AstraZeneca R&D site in Gothenburg Sweden.

If you have a few years of postdoctoral experience or have recently completed your PhD this position will give you a superb opportunity to advance your career to the next level while influencing AstraZenecas digital Design Make Test and Analyze (DMTA) platforms. Are you ready to join us

What you will do:
You will have a profound impact on early drug discovery across AstraZeneca by helping us build large multi-modal foundation models leveraging our massive collection of internal data to help design and prioritize new molecules for synthesis.

Your contributions will include:
Design and implement state-of-the-art methods for modeling molecular data at large scale integrating ADMET & potency measurements experimental structures physics-based simulation results and more
Develop and execute workflows for training fine-tuning and fast inference at scale on multi-GPU systems
Critically evaluate performance and drive data acquisition strategies using active leaning to maximize accuracy
Work closely with cross-functional project teams to apply these methods to challenging drug discovery problems
Plan write and publish high quality scientific papers
Actively participate in mentoring the cohort of Postdocs PhD students and Master Thesis students in the team

Essential requirements for the role:
We believe that you have a true passion for AI science and drug design. You have a PhD degree in a relevant computational discipline and experience in using modern machine learning methods to solve scientific problems with real impact.

More specifically you have:

A PhD in machine learning chemistry physics bioinformatics or another relevant quantitative discipline or comparable industrial experience
In-depth scientific knowledge of modern machine learning/deep learning techniques for example graph models generative models foundational models diffusion models or transformers demonstrated by track record of project deliveries or publications and external presentations
Experience in Python development
Excellent written and verbal communication skills
Highly collaborative mindset

Desirable requirements for the role:
Background in cheminformatics/molecular property prediction highly advantageous
Familiar with drug discovery and/or early development in industry or in a leading academic institution
Collaborative software development experience
Experience with workflow automation and high-performance computing environments
Genuine interest in driving science forward through supervising Master thesis and PhD students

Work policy:
When we put people in the same room we unleash bold thinking with the power to inspire life-changing -person working gives us the platform we need to connect work at pace and challenge perceptions. Thats why we work on average a minimum of three days per week from the office. This role is located in Gothenburg Sweden and is not available for remote work or with travel or commuting support.

Why AstraZeneca
At AstraZeneca were dedicated to being a Great Place to Work. Where you are empowered to push the boundaries of science and unleash your entrepreneurial spirit. Theres no better place to make a difference to medicine patients and society. An inclusive culture that champions diversity and collaboration. Always committed to lifelong learning growth and development.

Whats next
If this sounds like the place and role for you apply today! We look forward to get to know you better! Welcome with your application no later than May 22 2026. For more information about the role please contact hiring manager Jon Paul Janet

Date Posted

Closing Date