Design and implement scalable infrastructure and software solutions to support large-scale AI models and agentic systems across the entire software development life cycle.
Design and implement sophisticated machine learning and deep learning pipelines that can handle massive amounts of data with optimal resource utilization.
Develop and maintain cloud-native architectures that enable seamless deployment and scaling of AI/ML workloads.
Deliver robust, tested and high-performance code in an agile environment.
Liaise with AI/ML engineers, data scientists, and domain experts to ensure fit-for-purpose infrastructure and data pipelines for cutting-edge scientific projects.

A degree in a quantitative or engineering discipline (e.g., computer science, computational biology, bioinformatics, engineering, among others); OR equivalent work experience as a professional software engineer.
Demonstrated advanced programming expertise in Python and in developing and delivering robust, scalable software solutions.
Experience with cloud platforms (AWS, GCP, Azure) and cloud-native architectures.
Passion for software design and commitment to the development of reusable, scalable, and testable software components.
Basic understanding of at least one major deep learning framework (PyTorch, JAX, TensorFlow).
Knowledge of command-line tools and shell scripting.
Knowledge of software engineering best practices, including continuous integration (CI) and continuous deployment (CD), containerization, and infrastructure as code.
Strong problem-solving and debugging skills, and experience working in cluster settings or cloud-based environments.
Fluency in English.

AIML Software Engineer, AI for Science

Key skills