Our client is advancing the boundaries of healthcare and medical science through a combination of biology and artificial intelligence expertise. They are expanding a portfolio of successful initiatives across drug discovery, design, and protein engineering. Notably, they are developing the next-generation vaccines and biopharmaceuticals for cancer treatment and prevention and therapy of infectious diseases.
Responsibilities
- Follow and communicate the latest developments in machine learning and biology. Design, implement and deliver performant and scalable algorithms based on state-of-the-art machine learning and neural network methodologies using distributed computing systems on-premises and cloud infrastructures.
- Conduct rigorous data analysis and statistical modelling to explain and improve models.
- Report results clearly and efficiently, both internally and externally, verbally and in writing.
- Write high-quality, maintainable, and modular code together with precise documentation.
- Actively collaborate with the business development team in the pre-sales activities, including but not limited to presenting the company to new prospective clients, writing decks and proposals, participating in calls and meetings, and representing the company in conferences/events.
Requirements
- Master's, PhD degree or equivalent experience in applied mathematics, computer science, or related scientific field.
- 1+ year experience in deep learning demonstrated via previous work, publications, contributions to open source projects, or coding competitions.
- Strong software engineering experience (Python, Docker, Linux).
- Strong experience using a machine learning framework (PyTorch, JAX, TensorFlow).
- Desire to work with biological applications.
- Data science and statistics experience including data visualisations, statistical testing, etc.
- Excellent communication skills in English.
- Appropriate work permit for the considered location.
Desirable
- Knowledge of molecular biology, structural biology, -omics, immunology, or a related discipline.
- Knowledge of current research in deep learning applied to biology.
- Specialist computing knowledge, such as high-volume data storage and processing, high-performance computing, or deployment.