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Internship Machine Learning Jobs in Florida (NOW HIRING)

Medical Company

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Chief Technology Officer

West Palm Beach, FL · On-site

$180K/yr

... machine learning engineering, and product engineering across the web, mobile, and extension surfaces. · Build a structured internship-to-hire pipeline that converts high-performing interns into ...

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chicosfas

Analytics Specialist

Fort Myers, FL

... and machine learning personalization. FUNCTIONAL RESPONSIBILITIES: * Assists in the design ... application (internship, co-op, work experience) in an analytics field * Strong SQL skills

chicosfas

Analytics Specialist

Fort Myers, FL · On-site

... and machine learning personalization. FUNCTIONAL RESPONSIBILITIES: * Assists in the design ... application (internship, co-op, work experience) in an analytics field * Strong SQL skills

Persona AI

Robotics Data Pipeline Intern

Pensacola, FL · On-site

This is not a "fetch coffee and shadow engineers" internship. You'll own real work and ship real ... D. in Computer Science, Data Engineering, Machine Learning, Robotics, or a related field * Solid ...

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All JobsMachine Learning JobsInternship Machine Learning JobsInternship Machine Learning Jobs in Florida

Internship Machine Learning information

See Florida salary details

$19.1K

$31.8K

$65.8K

How much do internship machine learning jobs pay per year?

As of Jul 19, 2026, the average yearly pay for internship machine learning in Florida is $31,822.00, according to ZipRecruiter salary data. Most workers in this role earn between $24,300.00 and $34,400.00 per year, depending on experience, location, and employer.

What is the difference between Internship Machine Learning vs Data Science Intern?

AspectInternship Machine LearningData Science Intern
Required CredentialsBasic programming, introductory ML knowledgeStatistics, programming, data analysis basics
Work EnvironmentHands-on ML model development, codingData analysis, visualization, reporting
Industry UsageTech, AI companies, research labsBusiness, finance, healthcare sectors

Internship Machine Learning focuses on developing and implementing machine learning models, requiring programming and ML fundamentals. Data Science Internships involve analyzing data, creating reports, and supporting decision-making. Both roles are common in tech and research industries, but ML internships are more specialized in model building, while Data Science internships emphasize data analysis and visualization.

What are the key skills and qualifications needed to thrive as an Internship Machine Learning, and why are they important?

To thrive as a Machine Learning Intern, you generally need a solid grounding in mathematics, programming (especially Python), and familiarity with machine learning concepts, often supported by coursework or relevant projects. Experience with tools and libraries like TensorFlow, scikit-learn, and Jupyter Notebooks, as well as knowledge of version control systems like Git, is typically expected. Strong problem-solving skills, willingness to learn, and effective communication set outstanding interns apart. These skills and qualities enable interns to contribute meaningfully to projects, adapt quickly, and collaborate well within technical teams.

What are internship machine learning positions?

Internship machine learning positions are temporary roles for students or recent graduates to gain hands-on experience in the field of machine learning. Interns typically work on real-world projects involving data analysis, model development, and algorithm implementation under the guidance of experienced professionals. These internships provide valuable exposure to machine learning tools, programming languages such as Python, and industry best practices. They are an excellent way to build technical skills, enhance your resume, and explore career opportunities in artificial intelligence and data science.

What types of projects can I expect to work on during a Machine Learning internship?

As a Machine Learning intern, you may work on a variety of projects such as data preprocessing and cleaning, developing and testing machine learning models, or assisting with research experiments. These projects often involve collaborating closely with data scientists and engineers, learning to use popular frameworks like TensorFlow or PyTorch, and presenting your findings to the team. The scope and complexity of your assignments will typically grow as you demonstrate proficiency and initiative, providing valuable real-world experience and networking opportunities.
What are the most commonly searched types of Machine Learning jobs in Florida? The most popular types of Machine Learning jobs in Florida are:
What are popular job titles related to Internship Machine Learning jobs in Florida? For Internship Machine Learning jobs in Florida, the most frequently searched job titles are:
What job categories do people searching Internship Machine Learning jobs in Florida look for? The top searched job categories for Internship Machine Learning jobs in Florida are:
What cities in Florida are hiring for Internship Machine Learning jobs? Cities in Florida with the most Internship Machine Learning job openings:
Internship Machine Learning jobs near you
Infographic showing various Internship Machine Learning job openings in Florida as of July 2026, with employment types broken down into 7% Internship, 1% As Needed, 69% Full Time, 20% Part Time, 2% Temporary, and 1% Contract. Highlights an 87% Physical, 1% Hybrid, and 12% Remote job distribution, with an average salary of $31,822 per year, or $15.3 per hour.
Applied Data Scientist

Applied Data Scientist

Professional Staffing Services

Orlando, FL • Remote

Contractor

Posted 4 days ago

Job description

Applied Data Scientist - Contract to Hire

Location: Florida (Remote but will need to travel to Orlando for your first day, and for occasional meetings and trainings. )

Employment Type: Full-Time, Pay: ~ 100K-150K

Sponsorship: Not Available (Now or in the future)

About The Company

Our client drives innovative, datadriven insights and scalable AI solutions across the entertainment ecosystem. The Data Science team partners with data engineering, marketing, product, and executive teams to transform audience data into actionable strategies and operational products.

A successful Applied Data Scientist thrives on both analytical creativity and production rigor. As a key member of our client's team, you will own endtoend modeling and deployment work-from the conceptual framing of business problems to data ingestion, model development, and reliable production delivery. Your work will directly shape how our company delivers value to clients and internal stakeholders.

Position Summary & Location Requirements

This is a Florida-based role. While the day-to-day work offers remote flexibility, candidates must reside in the state of Florida and meet the following travel requirements:

  • Day One: Ability to travel to Orlando, FL for your first day/onboarding.
  • Ongoing: Ability to travel to Orlando on occasion for collaborative meetings, trainings, and to support business needs.

Key Responsibilities

In this role, you will bridge the gap between business strategy and technical execution. Specifically, you will:

  • Model & Solution Development: Translate ambiguous business questions into structured analytical and ML solutions. Develop, validate, and optimize models impacting forecasting, segmentation, personalization, recommendation, or operational efficiency.
  • Production & MLOps: Build productionready pipelines and deploy models into scalable environments using robust MLOps practices (CI/CD, automated testing, monitoring), ensuring long-term lifecycle maintenance.
  • Collaboration & Communication: Partner cross-functionally to bridge business requirements and technical design. Communicate insights and technical decisions clearly to both technical and nontechnical stakeholders.
  • Documentation & Standards: Document all models, pipelines, and deployment processes comprehensively to ensure maintainability, reproducibility, and knowledge sharing.
  • Innovation: Stay ahead of emerging tools, techniques, and frameworks in ML/AI to influence best practices across the organization.

Core Qualifications

  • Education: Bachelor's degree in Computer Science, Statistics, Mathematics, or a related quantitative field.
  • Professional Experience: 5+ years of industry experience (excluding internships) in data science and machine learning, including proven ownership of model productization, monitoring, and iterative improvement.
  • Core ML Experience: 3+ years of building machine learning models for business applications (outside of academia), with deep expertise in both supervised and unsupervised learning algorithms.
  • Technical Stack:
  • Python: Strong programming skills with hands-on experience building, training, deploying, and monitoring ML models.
  • SQL: 2+ years of experience with database querying, data preparation, and analysis.
  • Data Warehousing: Working knowledge of large-scale platforms (e.g., Snowflake, SQL Server, BigQuery, Redshift).
  • Cloud Platforms: Familiarity with cloud environments (AWS, Azure, or GCP) and designing end-to-end ML pipelines from ingestion to production serving.
  • Execution Skills: Outstanding analytical skills to diagnose and resolve complex system issues, with a proven ability to manage multiple projects and prioritize tasks effectively.

What Sets You Apart (Preferred Qualifications)

  • Advanced Degree: Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative field.
  • Domain Expertise: Industry experience in entertainment or e-commerce, including domains such as theme parks, hospitality, live performances, ticketing, or retail marketplaces.
  • Advanced ML Architectures: Hands-on experience designing and deploying recommendation models (collaborative filtering, content-based, transformer-based) or working with data labeling, taxonomy design, and classification frameworks.
  • Generative AI: Familiarity with GenAI techniques, language modeling, or frameworks like AWS Bedrock and Hugging Face.
  • Deep MLOps Tooling: Advanced experience with tools like SageMaker, Lambda, Airflow, or MLflow, and the ability to guide architectural/strategic decisions for ML infrastructure.

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