✨ About The Role
- The role involves driving the full lifecycle of new scientific machine learning projects, including architecture design and model implementation.
- Responsibilities include developing auditable datasets and data-preparation pipelines, as well as training and experimentation.
- The position requires deployment of productized capabilities and maintaining existing scientific software packages.
- Collaboration with the Engineering Team to utilize cloud infrastructure is a key aspect of the job.
- The candidate will work closely with the client-facing Drug Discovery Team to conduct mission-critical computational scientific work.
âš¡ Requirements
- A post-graduate degree in a relevant scientific discipline such as physics, chemistry, biology, or computer science is essential.
- At least 5 years of professional experience in implementing, training, and deploying modern deep learning architectures is required.
- Experience with large datasets and distributed compute resources is necessary for success in this role.
- Proficiency in MLOps and data engineering best practices is expected.
- The ideal candidate should be able to write high-quality code quickly and efficiently.
- A desire to work in a fast-paced, collaborative environment with diverse perspectives is important.