Senior Machine Learning Engineer

Overview

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

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.

Corporate Overview:
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.

Responsibilities

Essential Functions

* 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.

Knowledge / Skills / Abilities

* 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 identifications of dependencies and the development of design documents for a product, application, service, or platform.

* Write efficient systems code and tests and able to debug distributed systems.

* Work on-call to monitor systems/product/service for degradation, downtime or interruptions.

* Innovative mindset with a keen eye for identifying opportunities for improvement.

* Ability to thrive in a fast-paced, dynamic environment and simultaneously handle multiple projects.

* Partner effectively with other engineers, product managers, and stakeholders.

Qualifications

Required

* Bachelor's degree in a relevant field.

* 5 years of experience in software engineering.

Qualifications (Preferred)

Preferred

* Experience working in complex academic medical center environments.

* Experience with large multimodal health datasets.

* Experience with lifecycle management in a fast-paced software environment.

* Experience in shipping products and scalable, reliable services.

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