Updated note from manager.
The candidates need to have engineering experience in deploying, monitoring, and tracking models in a real-world production environment at scale, along with a strong understanding of AI/ML programming and algorithms.
The position:
Machine Learning Engineer, client Computational Sciences / Computational Catalysts / Scientific Insights Engineering
The team:
At client Research & Early Development (gRED) we have initiated an exciting journey to bring together and further strengthen our computational talent and capabilities by forming a new, central organization
- gRED Computational Sciences (gCS). gCS is on a mission to partner across the organization to realize the potential of data, technology and computational approaches that will revolutionize how targets and therapeutics are discovered and developed, ultimately enabling novel treatments for patients across the world. We stand at the beginning of this exciting journey.
The Computational Catalysts group within gCS is a diverse, curious and action-driven team at the intersection of computation, engineering and science with ambition to advance our technical excellence.
The focus of the team is on partnering with the informatics and scientific communities to create a computational and data ecosystem that powers scientific discovery and accelerates decision making. We aim to modernize our ability to acquire, store, link, share, find and analyze data across the organization through scalable and integrated solutions that truly make every data point count.
The Opportunity:
Our group is seeking an exceptional Machine Learning Engineer with a passion for building machine learning algorithms and systems that will transform the drug discovery process.
We are looking for someone who is not only passionate about technical problem
-solving but also has a consistent record of delivering innovative solutions in machine learning.
The ideal candidate should have extensive experience in developing, deploying, and maintaining models and be able to foster a culture of innovation, collaboration, and excellence across the broader gCS organization.
The group provides a dynamic and challenging environment for multidisciplinary research including access to heterogeneous data sources, close links to top academic institutions around the world, as well as collaborations with internal client teams.
The right candidate will have experience working in different computing environments, including traditional HPC and AWS.
The candidate will have a good understanding of best practices in software engineering and will use it to provide solutions that are scalable and reusable.
The candidate will have experience working with diverse data in the biomedical space, including genetics, genomics, imaging, and clinical data.
The candidate will be comfortable working as part of a team and also independently.
What you will do:
Develop and deploy machine learning models in production environments, working closely with other engineers to ensure solutions are scalable, reliable, and built with best practices.
Solve core research engineering challenges including the design, implementation, and scaling of machine learning algorithms.
Collaborate with cross-functional teams including research scientists, computational biologists, and data engineers to solve complex problems.
Build solutions that allow stakeholders to interact with and analyze multimodal datasets.
Who you are:
Educational Background: B.S. in Computer Science, Machine Learning, Statistics, Mathematics, Physics, or a related field (Graduate degree preferred).
Experience: Proven track record with 4+ years of experience developing and applying ML models in an industry setting.
Technical Skills:
Proficiency in Python
Experience with Weights and Biases
Proficiency in MLOps workflows (e.g., familiar with code version control, high-performance compute infrastructures, and machine learning experiment monitoring workflows)
Extensive experience with machine learning frameworks and libraries (e.g., JAX, PyTorch, PyTorch Lightning, Tensorflow).
Strong background in statistics, probabilistic modeling, and data analysis.
Soft Skills:
Strong communication skills, with the ability to effectively communicate technical concepts to both technical and non-technical audiences as well as interfacing with scientific and engineering leadership
Experience collaborating with external scientific partners, such as academic institutions or industry research groups
A passion for solving complex technical problems and a commitment to staying up-to-date with the latest developments in machine learning.