Quantitative Research Engineer
Chicago, IL, United States
Full Time Mid-level / Intermediate USD 146K - 205K
Northwestern Memorial Healthcare
Northwestern Medicine is a leader in quality healthcare and service, bringing together faculty, physicians and researchers to support and advance that care through leading-edge treatments and breakthrough discoveries.Company Description
At Northwestern Medicine, every patient interaction makes a difference in cultivating a positive workplace. This patient-first approach is what sets us apart as a leader in the healthcare industry. As an integral part of our team, you'll have the opportunity to join our quest for better healthcare, no matter where you work within the Northwestern Medicine system. At Northwestern Medicine, we pride ourselves on providing competitive benefits: from tuition reimbursement and loan forgiveness to 401(k) matching and lifecycle benefits, we take care of our employees. Ready to join our quest for better?
Job Description
Quantitative Research Engineers for Chicago, IL location. Design and develop technology that is impactful, including software or hardware solutions in a team-based environment. Working closely with the end user to support and improve workflows. Stay current on development tools, programming techniques and computing equipment. Participate in educational opportunities and read professional publications. Technological environment: manipulating a broad range of medical imaging data types (2D, 3D, & 4D images) and modalities (X-Ray, CT, MRI & PET); Traditional ML techniques (gradient descent and others); Deep neural network architectures (transformers, convolutional neural networks, recurrent neural networks); High-level machine learning APIs (PyTorch); Design and optimization of data pipelines (cloud and on-prem hardware); Python; C; Linux /Unix/ BSD and platform specific tooling; git/Github; Containers; Developing deep neural networks for medical imaging tasks (classification, segmentation, object detection, generation); AI/ML models and FDA regulatory/clearance process; standard medical imaging data formats (DICOM).
Job Code Reference : REF81095F
Qualifications
Required:
Bachelor’s degree in Computer Science, Engineering, Math, Physics or related technical field plus 2 years of experience in software development required. Skills required: 2 years experience with: ML/AI related to healthcare; Experience must include: Manipulate a broad range of medical imaging data types (2D, 3D, & 4D images) and modalities (X-Ray, CT, MRI & PET); Traditional ML techniques including gradient descent; Deep neural network architectures (transformers, convolutional and recurrent); High-level machine learning APIs (PyTorch); Design and optimization of data pipelines (cloud and on-prem hardware); Python or C; Linux/Unix/BSD and platform specific tooling; git/Github; Containers; developing deep neural network for medical imaging tasks (classification segmentation, object detection, generation; AI/ML models and FDA regulatory/clearance process; standard medical imaging formats (DICOM). Some hybrid work available. Occasional international and domestic travel for conferences or meeting with partners required. Background check and drug test required. $146,723/yr- $205,412/yr.
Additional Information
Northwestern Medicine is an affirmative action/equal opportunity employer and does not discriminate in hiring or employment on the basis of age, sex, race, color, religion, national origin, gender identity, veteran status, disability, sexual orientation or any other protected status.
Benefits
We offer a wide range of benefits that provide employees with tools and resources to improve their physical, emotional, and financial well-being while providing protection for unexpected life events. Please visit our Benefits section to learn more.
Tags: APIs Architecture Classification Computer Science Data pipelines DICOM Engineering Git GitHub Linux Machine Learning Mathematics ML models Physics Pipelines Python PyTorch Research Transformers
Perks/benefits: Career development Conferences Team events
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