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Job Description: Machine Learning Engineer- job post
ECU Health
3.7
3.7 out of 5 stars
Greenville, NC 27835•Remote
$106,745.60 - $176,134.40 a year - Full-time
ECU Health
Greenville, NC 27835•Remote
$106,745.60 - $176,134.40 a year

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Profile insights
Here’s how the job qualifications align with your profile.
Skills

Visual Studio

TensorFlow

Spark

Scikit-learn

R

PyTorch

Pandas

NumPy

Model training

Machine learning frameworks

Machine learning

Jupyter

Distributed systems

Distributed computing

Data science

Data manipulation

Data analysis skills

Communication skills

Analysis skills

SQL

Python

Kubernetes

AWS

AI
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Education

Bachelor's degree
 (Required)
 
Job details
Here’s how the job details align with your profile.
Pay

$106,745.60 - $176,134.40 a year
Job type

Full-time
 
Full job description
ECU Health

About ECU Health

ECU Health is a mission-driven, 1,708-bed academic health care system serving more than 1.4 million people in 29 eastern North Carolina counties. The not-for-profit system is comprised of 13,000 team members, nine hospitals and a physician group that encompasses over 1,100 academic and community providers practicing in over 180 primary and specialty clinics located in more than 130 locations.

The flagship ECU Health Medical Center, a Level I Trauma Center, and ECU Health Maynard Children's Hospital serve as the primary teaching hospitals for the Brody School of Medicine at East Carolina University. ECU Health and the Brody School of Medicine share a combined academic mission to improve the health and well-being of eastern North Carolina through patient care, education and research.

Position Summary

The Machine Learning Engineer will develop, deploy, and optimize machine learning models to address business and clinical challenges using cloud-based platforms like Microsoft Azure and unified data ecosystems like Microsoft Fabric. Collaborating with data engineers, data scientists, and stakeholders, the engineer will utilize frameworks such as PyTorch, TensorFlow, and scikit-learn, alongside distributed computing tools like Apache Spark, to build scalable ML pipelines. The role encompasses data preparation, model training, deployment, and monitoring, with a focus on ethical AI practices, contributing to innovative solutions in a collaborative environment. The Machine Learning Engineer will play a crucial role in bridging the gap between raw data and actionable insights, contributing to the company's digital transformation and innovation efforts.

Responsibilities

Develop and implement machine learning models using frameworks like PyTorch, TensorFlow, and scikit-learn to solve business problems within cloud environments like Microsoft Azure and unified data platforms like Microsoft Fabric.
Design and optimize scalable ML pipelines using distributed computing frameworks like Apache Spark (PySpark) and libraries like SynapseML for large-scale data processing and model training.
Utilize experiment tracking tools like MLflow to log metrics, track experiments, and manage model versioning for reproducible results in cloud platforms like Microsoft Azure Machine Learning.
Prepare and preprocess data using data wrangling tools like Microsoft Fabrics Data Wrangler and unified data lakes like Microsoft OneLake, integrating external data sources via orchestration tools like Microsoft Azure Data Factory.
Deploy machine learning models to production using managed endpoints, container orchestration systems like Kubernetes, or inference optimization frameworks like ONNX Runtime.
Leverage cloud-based AI development platforms like Microsoft Azure AI Foundry or machine learning studios like Microsoft Azure Machine Learning Studio to build, test, and deploy models with automated ML and visual pipelines.
Integrate prebuilt AI services, such as those similar to Microsoft Azure OpenAI Service, into data science workflows to enhance generative AI and predictive analytics solutions.
Monitor and maintain deployed models, utilizing MLOps capabilities in platforms like Microsoft Azure Machine Learning to ensure performance, scalability, and governance.
Conduct exploratory data analysis and visualization using Python, R, or SQL in notebook environments like those in Microsoft Fabric to derive actionable insights.
Collaborate with data engineers and stakeholders to define data requirements and ensure seamless integration of ML solutions with unified data platforms like Microsoft Fabric.
Implement responsible AI practices, using tools for model interpretability, fairness assessment, and bias mitigation, similar to those in Microsoft Azure, to ensure ethical outcomes.
Optimize model performance by fine-tuning foundation models from model catalogs like those in Microsoft Azure (e.g., Hugging Face, Meta) for specific use cases.
Automate data orchestration and ML workflows using pipeline tools like Microsoft Azure Data Factory or data platform capabilities like those in Microsoft Fabric to streamline development.
Document ML workflows, model performance, and pipeline configurations to maintain transparency and support team collaboration.
Stay updated on advancements in cloud and data platform tools, integrating new features like those similar to Microsoft Prompt Flow or Copilot to enhance productivity and innovation.
Minimum Requirements

Bachelor's degree or higher in computer science, data science, engineering, mathematics, or a related field, or equivalent practical experience (e.g., self-taught programming, bootcamps, or relevant certifications).
8 years of relevant work experience required, with 2 years of experience in a relevant role demonstrating responsible AI Awareness preferred.
Programming Skills: Basic proficiency in Python for data analysis and model development. Familiarity with SQL for data querying is a plus but not required.
Machine Learning Knowledge: Foundational understanding of machine learning concepts (e.g., supervised/unsupervised learning, basic algorithms like linear regression or decision trees). Hands-on experience with at least one ML framework like scikit-learn, PyTorch, or TensorFlow through coursework, personal projects, or internships.
Data Handling: Experience with data manipulation and analysis using Python libraries like pandas or NumPy, or similar tools. Exposure to large datasets or distributed computing (e.g., Spark) is desirable but not mandatory.
Cloud Exposure: Familiarity with any cloud platform (e.g., Microsoft Azure, AWS, or Google Cloud) through academic projects, tutorials, or personal exploration. Willingness to learn cloud-based ML tools like Microsoft Azure Machine Learning or data platforms like Microsoft Fabric.
Problem-Solving: Strong analytical skills and enthusiasm for tackling complex problems, demonstrated through academic projects, hackathons, or personal initiatives.
Collaboration: Ability to work effectively in a team, with clear communication skills, as shown through group projects, internships, or volunteer work.
Learning Mindset: Eagerness to learn and adapt to new tools, frameworks, and platforms, with a proactive approach to professional development and training.
Responsible AI Awareness: Basic understanding of ethical considerations in AI (e.g., bias, fairness), with a willingness to deepen knowledge through training.
Tools: Comfort using development environments like Jupyter Notebooks or Visual Studio Code, with readiness to learn cloud-based notebook interfaces.
General Statement

It is the goal of ECU Health and its entities to employ the most qualified individual who best matches the requirements for the vacant position.

Offers of employment are subject to successful completion of all pre-employment screenings, which may include an occupational health screening, criminal record check, education, reference, and licensure verification.

We value diversity and are proud to be an equal opportunity employer. Decisions of employment are made based on business needs, job requirements and applicants qualifications without regard to race, color, religion, gender, national origin, disability status, protected veteran status, genetic information and testing, family and medical leave, sexual orientation, gender identity or expression or any other status protected by law. We prohibit retaliation against individuals who bring forth any complaint, orally or in writing, to the employer, or against any individuals who assist or participate in the investigation of any complaint.


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