We have an exciting opportunity to join our team as a Research Engineer.
We are looking for a highly motivated and passionate Machine Learning Engineer to join our team who has expertise in machine learning, computer vision, and software engineering. As a ML Engineer, you will be a part of the interdisciplinary group of research scientists, clinicians, and engineers. In close collaboration with your colleagues, you will help develop, validate, and deploy machine learning models. You will be responsible for building and maintaining the necessary infrastructure and software pipelines to facilitate this work. You will engage with interdisciplinary research topics and develop machine learning solutions for various complex and novel problems using large scale medical imaging datasets and datasets of other modalities. You will also act as a project leader on specific projects, where you will be responsible for creating the project plans, meeting project timelines, troubleshooting, and resolving technical problems. Leveraging your machine learning expertise, you will also provide technical machine learning advice to faculty and lab staff. Lastly, you will operate under standard procedures and protocols in regulatory and compliance. This is a unique opportunity to work in the interdisciplinary area of machine learning and healthcare as you will not only work on developing machine learning models but will also have an opportunity to deploy the models in real clinical workflows, thereby enabling you to make a real world impact by saving and/or improving the lives of real people.
- Provide technical guidance to faculty and lab staff on projects at the intersection of machine learning and radiology
- Act as a project leader be responsible for creating project plans, meeting deadlines, and resolving technical problems
- Be involved in fundamental data analysis to extract patterns in large swaths of data to acquire insights into the nature of the data, in turn enabling us to model it better
- Be involved in the model analysis to understand its deficiencies and thereby to propose refinements to improve the end outcome
- Be responsible for the entire pipeline associated with developing such algorithms, including extraction and curation of the datasets, implementing and training/validation/testing the machine learning models to ensure clinical relevancy, and deploying the models in real clinical setting
Additional Position Specific Responsibilities:
Create and maintain the necessary infrastructure and software pipelines
Adapt machine learning and neural network algorithms and architectures to best exploit modern parallel environments, such as GPUs and distributed clusters
- To qualify you must have a Master’s degree in computer science or equivalent or Bachelor’s degree with experience in machine learning research
- Thorough understanding of machine learning fundamentals, probability, statistics, and numerical optimization
- Experience in building machine learning models using large-scale datasets
- Experience in deploying machine learning models in real-world environments
- Experience in developing and debugging in scripting languages such as Python
- Familiarity of programming in low-level languages such as C/C++
- Experience working with machine learning and computer vision libraries, such as PyTorch, TensorFlow, and OpenCV
- Technical understanding of the DICOM standard (NIfTI, FSL etc), as well as some familiarity with radiology software, such as Vital, OsiriX, and PACS
- Prior exposure to or some domain knowledge in machine learning applications in medical imaging
- Strong communication and leadership skills
Qualified candidates must be able to effectively communicate with all levels of the organization.