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Machine Learning Developer Intern Jobs in Charlotte, NC

In this role, you will partner with Product, Engineering, Clinical,Operations, Marketing and Data Engineering to design, build, deploy, andoperatescalable machine learning and AI systems that power ...

Software Developer Intern 2027

Lowell, NC · On-site

$16.25 - $21.25/hr

... machine learning to generative AI-to help make organizations more responsive, productive, and ... Job Title Software Developer Intern 2027 Date posted 11-Aug-2026 Job ID 128497 City / Township ...

Software Developer Intern 2027

Lowell, NC

$16.25 - $21.25/hr

... machine learning to generative AI-to help make organizations more responsive, productive, and ... Job Title Software Developer Intern 2027 Date posted 11-Aug-2026 Job ID 128497 City / Township ...

The Hartford is seeking Senior AI Machine Learning Engineer to build Machine Learning Operations ... Master's degree in related field or 5+ years of equivalent experience in a research or DevOps ...

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Deep knowledge of supervised learning, unsupervised learning, feature engineering, model selection ... Familiar with machine learning curricula and common challenges such as understanding bias-variance ...

Showing results 41-60

Machine Learning Developer Intern information

See Charlotte, NC salary details

$24.9K

$41.6K

$86K

How much do machine learning developer intern jobs pay per year?

As of Sep 10, 2026, the average yearly pay for machine learning developer intern in Charlotte, NC is $41,592.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,700.00 and $44,900.00 per year, depending on experience, location, and employer.

What does a machine learning developer intern do?

A Machine Learning Developer Intern assists with developing, testing, and implementing machine learning models and algorithms under the guidance of experienced engineers or data scientists. Their tasks may include data preprocessing, model training, evaluating model performance, and helping deploy models into production environments. Interns often collaborate with team members to solve real-world problems using machine learning techniques and may also assist in researching new methodologies or optimizing existing solutions. This role provides hands-on experience in coding, data analysis, and applying theoretical concepts to practical scenarios.

What are the key skills and qualifications needed to thrive as a machine learning developer intern?

To thrive as a Machine Learning Developer Intern, you need a solid understanding of programming (especially Python), statistics, and machine learning concepts, often supported by coursework or relevant project experience. Familiarity with ML frameworks like TensorFlow or PyTorch, and tools such as Jupyter Notebooks and version control systems like Git, is typically expected. Strong analytical thinking, eagerness to learn, and effective communication help interns contribute to team projects and adapt quickly. These skills are essential for solving real-world problems, collaborating with teams, and building a foundation for a successful career in machine learning.

How do machine learning developer interns typically collaborate with data scientists and engineers during their internship?

Machine Learning Developer Interns often work closely with data scientists to understand the problem domain, gather relevant datasets, and select appropriate models. They also collaborate with software engineers to integrate machine learning solutions into existing systems, ensuring scalability and performance. Regular communication through stand-up meetings, code reviews, and collaborative platforms is common, allowing interns to learn best practices and receive feedback on their work. This teamwork not only enhances technical skills but also provides valuable exposure to real-world deployment and project lifecycle management.

What is the difference between Machine Learning Developer Intern vs Data Scientist Intern?

AspectMachine Learning Developer InternData Scientist Intern
Required CredentialsTypically pursuing or recently completed a degree in Computer Science, Data Science, or related fields; knowledge of programming languages like Python or JavaSimilar educational background; strong skills in statistics, programming, and data analysis
Work EnvironmentHands-on experience with ML models, algorithms, and software development in tech or research settingsData analysis, visualization, and interpretation in business or research contexts
Employer & Industry UsageTech companies, startups, research labs focusing on AI/ML projectsBusiness, finance, healthcare, and research organizations analyzing large datasets

Both roles involve working with data and programming, but Machine Learning Developer Interns focus more on building and deploying ML models, while Data Scientist Interns emphasize data analysis and insights. The roles often overlap, especially in tech environments, but their core tasks differ slightly.

Senior Machine Learning Scientist

Concord, NC • On-site, Remote

Teladoc
Fitness and Sports Centers • 1 - 5K employees

Full-time

Re-posted 3 days ago


Key responsibilities

  • Build, deploy, and operate scalable machine learning and AI systems, including data transformation pipelines, feature pipelines, model training, evaluation, deployment, monitoring, and retraining.

  • Collaborate with cross-functional teams to discover requirements, develop models, and improve clinical outcomes using insights and machine learning solutions.

  • Monitor production models and data pipelines for data quality, drift, latency, and performance, proactively resolving issues.


Job description

Join the team leading the next evolution of virtual care.

At Teladoc Health, you are empowered to bring your true self to work while helping millions of people live their healthiest lives.

Here you will be part of a high-performance culture where colleagues embrace challenges, drive transformative solutions, and create opportunities for growth. Together, we're transforming how better health happens.

Summary of Position

The Machine Learning effort is part of the Data Science team at Teladoc Health. In this role, you will partner with Product, Engineering, Clinical,Operations, Marketing and Data Engineering to design, build, deploy, andoperatescalable machine learning and AI systems that power business-critical decision making. You will own the end-to-end machine learning lifecycle:from data and feature engineering through deployment, monitoring, experimentation, and continuous improvement(across both batch and real-time production environments).Your efforts and contributions willhavea big impactonimproving member and provider experience on the Teladoc Health platform.

This is an opportunity to apply technical rigor, scalable data processing tools, and machine learning algorithms to solve real-world business problems while engineering, deploying, measuring, and iterating machine learning solutions in production.

Essential Duties and Responsibilities

  • Build production ready time series models to predict real time KPIs as well as build optimal decision actions to manage the provider network for clinical operations business optimization

  • Propose, evaluate and interpret the results of your work for clinical, product and business decision-makers and own outcomes

  • Collaborate closely with peers and stakeholders to discover and distill requirements of problem definitions, product features and architecture to improve clinical outcomes using insights and models

  • Develop modular, well-tested, production-quality software using Python, Spark and SQL to build scalable data engineering, feature engineering, machine learning and AI pipelines following software engineering best practices.

  • Design, develop, deploy and operate scalable production machine learning and AI systems, including data transformation pipelines, feature pipelines, model training, evaluation, deployment, monitoring, retraining, and experiment tracking. Ensure robust model lifecycle management through model versioning, MLflow, automated testing, CI/CD, and production monitoring.

  • Build and optimize scalable Spark and Databricks workloads, leveraging distributed computing best practices for large-scale data processing and real-time inference.

  • Design, evaluate and integrate Large Language Models (LLMs), retrieval-augmented generation (RAG), agentic workflows, and other AI capabilities where appropriate to solve business problems.

  • Monitor production models and data pipelines for data quality, feature drift, concept drift, latency, reliability, and business performance, proactively identifying and resolving issues.

Qualifications Expected for Position

  • 8+ years of experience as a Machine Learning Scientist, Data Scientist or in a similar role within SaaS or consumer technology companies.

  • A Master's degree or higher in computer science, operations research, machine learning, information systems, engineering, or a related field

  • Demonstrated depth of experience developing clean, robust, and reusable production-quality code using Python, Spark, and SQL.

  • Extensive experience designing, building and operating production machine learning systems, including scalable software, distributed data processing, reusable feature engineering pipelines, model deployment, monitoring and continuous improvement.

  • Strong understanding of statistical modeling, machine learning algorithms, experimentation, model evaluation, forecasting, and explainability techniques, with the ability to select appropriate approaches based on business and technical constraints.

  • Excellent data analysis skills and bias to deliver, measure and iterate using experimentation and statistical analysis

  • Strong system design skills with the ability to architect scalable, maintainable, and observable machine learning solutions.

  • Ability to translate machine learning solutions into measurable business outcomes and effectively communicate technical decisions, tradeoffs, and expected value to both technical and business stakeholders.

Bonus Qualifications

  • Hands-on experience with modern data and ML platforms such as Databricks, MLflow, Delta Lake, Airflow, Terraform, or equivalent cloud-native technologies.

  • Experience building AI-powered applications using Large Language Models (LLMs), embeddings, vector databases, retrieval-augmented generation (RAG), agentic workflows, or equivalent AI technologies is highly desirable.

  • Experience applying machine learning, forecasting, optimization, or decision science techniques to large-scale operational, logistics, marketplace, or network optimization problems.

  • Experience working with healthcare data (e.g., claims or EHR) is a plus.

  • Great active listening skills to infer product/business needs and underlying context.

  • Ability to collaborate effectively with peers, and respect for member privacy.

The base salary range for this position is$150,000 - $175,000. In addition to a base salary, this position is eligible for a performance bonus and benefits (subject to eligibility requirements) listed here: Teladoc Health Benefits 2026.Total compensation is based on several factors including, but not limited to, type of position, location, education level, work experience, and certifications.This information is applicable for all full-time positions.

#LI-SS2 #LI-Remote

We follow a Flexible Vacation Policy, intended for rest, relaxation, and personal time. All time off must be approved by your manager prior to use. You will also receive 80 hours of Paid Sick, Safe, and Caregiver Leave annually. This applies to full-time positions only. If you are applying for a part-time role, your recruiter can provide additional details.

As part of our hiring process, we verify identity and credentials, conduct interviews (live or video), and screen for fraud or misrepresentation. Applicants who falsify information will be disqualified.

Teladoc Health will not sponsor or transfer employment work visas for this position. Applicants must be currently authorized to work in the United States without the need for visa sponsorship now or in the future.

Why join Teladoc Health?

  • Teladoc Health is transforming how better health happens. Learn how when you join us in pursuit of our impactful mission.

  • Chart your career path with meaningful opportunities that empower you to grow, lead, and make a difference.

  • Join a multi-faceted community that celebrates each colleague's unique perspective and is focused on continually improving, each and every day.

  • Contribute to an innovative culture where fresh ideas are valued as we increase access to care in new ways.

  • Enjoy an inclusive benefits program centered around you and your family, with tailored programs that address your unique needs.

  • Explore candidate resources with tips and tricks from Teladoc Health recruiters and learn more about our company culture by exploring #TeamTeladocHealth on LinkedIn.

As an Equal Opportunity Employer, we never have and never will discriminate against any job candidate or employee due to age, race, religion, color, ethnicity, national origin, gender, gender identity/expression, sexual orientation, membership in an employee organization, medical condition, family history, genetic information, veteran status, marital status, parental status, or pregnancy). In our innovative and inclusive workplace, we prohibit discrimination and harassment of any kind.

Teladoc Health respects your privacy and is committed to maintaining the confidentiality and security of your personal information. In furtherance of your employment relationship with Teladoc Health, we collect personal information responsibly and in accordance with applicable data privacy laws, including but not limited to, the California Consumer Privacy Act (CCPA). Personal information is defined as: Any information or set of information relating to you, including (a) all information that identifies you or could reasonably be used to identify you, and (b) all information that any applicable law treats as personal information. Teladoc Health's Notice of Privacy Practices for U.S. Employees' Personal information is available at this link.


Teladoc logo

About Teladoc

Sourced by ZipRecruiter

Industry

Fitness and sports centers

Company size

1,001 - 5,000 Employees

Headquarters location

New York, NY, US

Year founded

2002