Senior Machine Learning Engineer

Overview

The Chief Innovation Office (CIO) at University of Utah Health (U of U Health) would serve as the strategic hub for advancing healthcare innovation, digital transformation, research commercialization, and new models of patient care. The office would connect clinical operations, research, education, and technology to accelerate the delivery of innovative solutions that improve health outcomes, patient experiences, and operational efficiency.

• Senior Machine Learning Engineers leverage their engineering expertise to solve a variety of technical problems for some of the most challenging and impactful projects in healthcare informatics and machine learning.
• You will work on a specific project critical to our needs with the opportunity to switch teams and projects as you and our fast‑paced business grow. We need machine learning engineers who are versatile, rigorous, display leadership qualities and are enthusiastic to take on new problems.
• You will design, develop, test, deploy, maintain and enhance machine learning and AI solutions.
• You will be part of the Innovation Office, a product factory inside the health system of the University of Utah.

Location: 102 Tower (Downtown SLC)

Schedule: Monday - Friday 9 AM - 5 PM

• Save 50% on Tuition (Tuition reduced for eligible employees attending the University of Utah)
• Health Coverage, Dental Coverage, Life Insurance
• Retirement
• Paid Time Off
• 11 Paid Holidays per year

University of Utah Health is an integrated academic healthcare system with five hospitals including a level 1 trauma center, eleven community health centers, over 1,600 providers, and a health plan serving over 200,000 members. University of Utah Health is nationally ranked and recognized for our academic research, quality standards and overall patient experience. In addition to our clinical delivery system, we have a School of Medicine, School of Dentistry, College of Nursing, College of Pharmacy, and College of Health providing education and training for over 1,250 providers annually.

We have over 2 million patient visits annually and research grants exceeding $350 million. University of Utah Hospitals and Clinics represents our clinical operations for the larger health system.

As a patient-focused organization, University of Utah Health exists to enhance the health and well-being of people through patient care, research and education. Success in this mission requires a culture of collaboration, excellence, leadership, and respect. University of Utah Health seeks staff that are committed to the values of compassion, collaboration, innovation, responsibility, integrity, quality and trust that are integral to our mission.

EO/AA

We encourage candidates to submit resumes that accurately reflect their own skills, experience, and accomplishments. The information provided during the application process must be truthful and authentic, as qualifications may be verified during the hiring process.

• Creating, managing, maintaining, and refactoring ML codebases, pipelines, and workflows for the innovation office.
• Collaborating closely with research, medical, and engineering staff to design and implement ML approaches.
• Implementing scalable ML methods and workflows for high-performance computing (HPC) resources, in close collaboration with research staff and technical staff.
• Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
• Troubleshooting data analysis issues, including implementation issues, hyper-parameter choices and modeling decision.
• Assisting in preparation of manuscripts and dissemination of results in the appropriate venues.
• Conducing tasks independently and communicate optimally to team members and stakeholders.
• Developing deployable solutions for the health care system.

• Hands‑on coding in C++, Python, PyTorch, or Tensorflow.
• Foundational understanding of supervised and unsupervised learning, reinforcement learning, and machine learning.
• Independently execute in the face of ambiguity.
• Leads…