About this role
Summary: Builds the engineering foundations that enable advanced AI research at scale, designing pipelines for preparing scientific datasets and training and evaluating large foundation and generative models.
Work Your Magic with us!
Ready to explore, break barriers, and discover more? We know you’ve got big plans – so do we! Our colleagues across the globe love innovating with science and technology to enrich people’s lives with our solutions in Healthcare, Life Science, and Electronics. Together, we dream big and are passionate about caring for our rich mix of people, customers, patients, and planet. That's why we are always looking for curious minds that see themselves imagining the unimaginable with us.
United As One for Patients, our purpose in Healthcare is to help create, improve and prolong lives. We develop medicines, intelligent devices and innovative technologies in therapeutic areas such as Oncology, Neurology and Fertility. Our teams work together across 6 continents with passion and relentless curiosity in order to help patients at every stage of life. Joining our Healthcare team is becoming part of a diverse, inclusive and flexible working culture, presenting great opportunities for personal development and career advancement across the globe.
Your role
In this role, you will build the engineering foundations that enable advanced AI research at scale, working at the interface between scientific data, foundation models, generative AI, and robust computational infrastructure.
You will design and implement pipelines for preparing complex scientific datasets for foundation-model training and evaluation, transforming heterogeneous biomedical and R&D data into high-quality, traceable, reproducible, and AI-ready datasets. You will work closely with AI scientists and domain experts to define data representations, preprocessing strategies, quality controls, dataset versions, training/test splits, and benchmark datasets appropriate for different modeling tasks.
You will build, optimize, and operate workflows for training, fine-tuning, and systematically evaluating large foundation and generative models in GPU-accelerated cloud and compute environments. You will support distributed training, experiment management, model and dataset versioning, reproducibility, performance optimization, and systematic comparison of models.
You will develop reusable infrastructure for benchmarking models across molecular, biological, imaging, and multimodal applications. This will include automated evaluation pipelines, experiment tracking, quantitative metrics, regression testing, and mechanisms for identifying model failures and performance changes as models and datasets evolve.
You will build the engineering capabilities that enable models and research code to operate effectively within an agentic scientific environment, connecting models, scientific tools, data, and computational services. You will work closely with research scientists and enterprise IT and platform teams to turn experimental methods into robust, reusable research capabilities.
Who you are
- Master's degree or PhD in computer science, machine learning, data engineering, computational science, bioinformatics, or a related quantitative discipline.
- At least 5 years of relevant industry experience in machine learning engineering, AI engineering, data engineering, or large-scale scientific computing.
- Strong hands-on experience building and operating training and evaluation pipelines for deep-learning or foundation models.
- Excellent Python and software-engineering skills.
- Experience with modern deep-learning frameworks such as PyTorch or JAX and GPU-accelerated computing.
- Practical experience with cloud-based ML environments, containers, distributed computation, experiment tracking, version control, testing, and reproducible ML workflows.
- Demonstrated ability to transform complex raw datasets into reliable AI-ready training and benchmark datasets.
- Experience with large-scale or multimodal scientific data, including molecular, biological, imaging, clinical, or related datasets, is highly desirable.
- Experience building evaluation frameworks and automated benchmarking systems for AI models.
- Experience integrating models with APIs, scientific software, tools, agents, or other AI-enabled workflows is an advantage.
- Strong collaboration skills and the ability to work effectively with research scientists, domain experts, software engineers, data teams, and enterprise technology teams.
What we offer: We are curious minds that come from a broad range of backgrounds, perspectives, and life experiences. We believe that this variety drives excellence and innovation, strengthening our ability to lead in science and technology. We are committed to creating access and opportunities for all to develop and grow at your own pace. Join us in building a culture of inclusion and belonging that impacts millions and empowers everyone to work their magic and champion human progress!
Apply now and become a part of a team that is dedicated to Sparking Discovery and Elevating Humanity!