Machine Learning Engineer

Consortia; Charlotte (NC); Hybrid; USD 170,000 - 225,000; Full time

Benefits offered by this job

Comprehensive healthcare

Retirement plans

Flexible work schedules

Ongoing professional development

Job summary

Consortia is seeking a Machine Learning Engineer in Charlotte, NC, offering a role that develops and deploys ML-driven healthcare solutions. You will design pipelines, prototype models, and collaborate across teams to deliver practical, scalable AI for healthcare workflows.

The position emphasizes robust code, scalable architectures, and delivering high-performance ML outputs in enterprise settings, with flexible work options including remote/hybrid.

Qualifications

• 3+ years of experience developing and deploying ML-driven products with robust outputs.
• Design, prototype and optimise ML architectures that integrate diverse data sources and models.

Responsibilities

• Design and optimise ML pipelines that handle millions of daily requests.
• Develop groundbreaking ML solutions beyond standard applications.
• Collaborate on end-to-end ML model development from research to deployment.
• Work with custom ML models to streamline administrative workflows in healthcare.

Skills

ML development

Model deployment

System architecture

Clean code

Job description

Machine Learning Engineer – Healthcare

Salary Range: $170,000 to $225,000

Location: Charlotte, NC

Are you passionate about developing innovative Machine Learning (ML) solutions in the healthcare sector? Our client, a leader in AI and large language model (LLM) technology, is transforming one of the USA's largest industries by drastically improving the speed and efficiency of medical workflows. Through advanced ML models and intelligent agents, their technology reduces hours-long processes to minutes, driving impactful automation in healthcare.

The Role

As a Machine Learning Engineer, you will:

• Design and optimise ML pipelines that handle millions of daily requests, powering automation in complex, data-driven tasks.
• Develop groundbreaking ML solutions that go beyond standard applications, tackling unique challenges with limited precedents.
• Collaborate on end-to-end ML model development, from research through deployment, to solve practical problems for major healthcare clients.
• Work with custom ML models to streamline administrative workflows, enhancing efficiency and reducing operational costs across healthcare systems.

About You

You’re an ML professional who:

• Has 3+ years of experience in developing and deploying ML-driven products, with a focus on robust, high-performance outputs.
• Is skilled at designing, prototyping, and optimizing ML architectures that integrate diverse data sources and models, ensuring consistent and accurate results.
• Has deep expertise in applying ML and AI research to practical, scalable product development in enterprise environments.
• Can break down complex challenges into streamlined, scalable architectures and write clean, reusable code.
• (Bonus) Brings experience within the healthcare industry or working on healthcare-specific ML applications.

Key Information

• Job Title: Machine Learning Engineer
• Location: Charlotte, NC
• Work Policy: Flexible; remote and hybrid options available
• Salary: $170,000 - $225,000
• Benefits: Comprehensive healthcare, retirement plans, flexible work schedules, ongoing professional development.

**Please note that we are currently unable to provide sponsorship for this requirement**

Consortia is a specialist recruitment agency with consultants focused on global roles within UX, Product, Data, and Engineering markets. If this Machine Learning Engineer job in Charlotte, NC, doesn’t align with your preferences, but you’re open to exploring other opportunities, please still register by applying to this role so we can match you to other requirements.

Kindly be aware that we cannot respond individually due to the high volume of applications; however, even if we do not contact you to move forward for this role, we will keep your details for future reference when a more suitable opportunity becomes available.