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Internship Graduate Machine Learning Jobs in Boca Raton, FL

Varsity Tutors

Data Science Tutor

Miramar, FL · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... graduate programs. * Conceptual Teaching & Problem-Solving: Skilled at teaching the full data ...

Varsity Tutors

Data Science Tutor

Sunrise, FL · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... graduate programs. * Conceptual Teaching & Problem-Solving: Skilled at teaching the full data ...

Varsity Tutors

Data Science Tutor

Fort Lauderdale, FL · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... graduate programs. * Conceptual Teaching & Problem-Solving: Skilled at teaching the full data ...

Varsity Tutors

Data Science Tutor

Cooper City, FL · Remote

$18 - $40/hr

Deep knowledge of statistical analysis, data wrangling, exploratory data analysis, machine learning ... graduate programs. * Conceptual Teaching & Problem-Solving: Skilled at teaching the full data ...

ASR Group

Sr. Data Scientist

Belle Glade, FL · Remote

... machine learning, and data science solutions that improve sugarcane research and operational decision-making. This role is intended for a highly capable professional with graduate-level training, who ...

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All JobsGraduate Machine Learning JobsInternship Graduate Machine Learning JobsInternship Graduate Machine Learning Jobs in Boca Raton, FL

Internship Graduate Machine Learning information

See Boca Raton, FL salary details

$24.2K

$40.4K

$83.5K

How much do internship graduate machine learning jobs pay per year?

As of Sep 8, 2026, the average yearly pay for internship graduate machine learning in Boca Raton, FL is $40,410.00, according to ZipRecruiter salary data. Most workers in this role earn between $30,800.00 and $43,700.00 per year, depending on experience, location, and employer.

What is an internship graduate machine learning?

Internship Graduate Machine Learning positions are entry-level roles designed for recent graduates or students who have completed coursework in machine learning, data science, or related fields. These internships provide hands-on experience working with real-world data, building and testing machine learning models, and collaborating with experienced professionals. Interns gain exposure to industry-standard tools and techniques, helping them bridge the gap between academic learning and practical application. Such positions are valuable for building a portfolio, networking, and enhancing job prospects in the rapidly growing field of artificial intelligence.

What types of projects do internship graduate machine learning roles typically involve, and how are responsibilities structured within the team?

Internship Graduate Machine Learning roles often focus on supporting ongoing research or development projects, such as building predictive models, cleaning and analyzing data, or prototyping algorithms. Interns usually collaborate closely with data scientists and engineers, contributing to specific project milestones while learning best practices in model development and deployment. Responsibilities are often structured to allow for mentorship and feedback, with interns participating in regular team meetings, code reviews, and brainstorming sessions. This collaborative environment provides valuable exposure to real-world machine learning workflows and helps interns build both technical and soft skills relevant to the field.

What are the key skills and qualifications needed to thrive as an internship graduate machine learning, and why are they important?

To thrive as an Internship Graduate in Machine Learning, you typically need a strong background in mathematics, programming (especially Python), and familiarity with algorithms and data structures, often supported by coursework or a degree in computer science, statistics, or a related field. Hands-on experience with machine learning frameworks like TensorFlow or PyTorch, and knowledge of tools such as Jupyter Notebooks and version control systems like Git, are highly valued. Curiosity, problem-solving, teamwork, and effective communication are crucial soft skills to excel in collaborative and innovative environments. These competencies enable interns to contribute to real-world projects, adapt to fast-changing technologies, and communicate findings clearly within interdisciplinary teams.

What is the difference between Internship Graduate Machine Learning vs Data Analyst?

AspectInternship Graduate Machine LearningData Analyst
Required CredentialsDegree in Computer Science, Data Science, or related field; basic knowledge of programming and statisticsDegree in Statistics, Mathematics, or related field; proficiency in data visualization and analysis tools
Work EnvironmentTech companies, research labs, startups; project-based, collaborative teamsBusiness, finance, marketing sectors; focus on reporting and data interpretation
Employer & Industry UsageUsed in tech, AI, and research industries for developing machine learning modelsCommon in corporate, finance, and consulting firms for data-driven decision making

While both roles involve working with data, an Internship Graduate Machine Learning focuses on developing algorithms and models using programming skills, often in tech environments. In contrast, a Data Analyst emphasizes interpreting data, creating reports, and supporting business decisions. The roles overlap in data handling but differ in technical depth and application focus.

What cities near Boca Raton, FL are hiring for Internship Graduate Machine Learning jobs?

Cities near Boca Raton, FL with the most Internship Graduate Machine Learning job openings:

Internship Graduate Machine Learning jobs near you
Infographic showing various Internship Graduate Machine Learning job openings in Boca Raton, FL as of July 2026, with employment types broken down into 1% As Needed, 72% Full Time, 24% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $40,410 per year, or $19.4 per hour.

Campus Undergraduate Full-Time Engineer - 2027 Software Engineer I, Enterprise Technology Service...

American Express

Sunrise, FL

Full-time

Medical, Dental, Vision, Life, Retirement

Posted 6 days ago

American Express rating

8.6

Company rating: 8.6 out of 10

Based on 37 frontline employees who took The Breakroom Quiz

26th of 154 rated financial services

Job description

At American Express, we empower technologists to learn, innovate, and make an impact from day one. As a Software Engineer in the full-time Technology Graduate Program, you'll join a 12-month technical and leadership development experience while contributing as a full-time colleague on an Enterprise Technology Services team. You'll build software, collaborate with Agile teams, and help deliver secure, reliable, customer-first technology that supports the future of payments.  

About the Team 

Enterprise Technology Services teams build, operate, and modernize the technology that helps American Express deliver trusted, secure, and customer-first products and services. Software Engineers may support teams across backend engineering, frontend engineering, cloud engineering, mobile, AI/ machine learning, data-oriented engineering, infrastructure-adjacent engineering, or full-stack product development. You will design, code, test, improve, and support applications and services that help customers, colleagues, and partners get work done reliably and securely. 

At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. From delivering differentiated products to providing world-class customer service, we operate with a strong risk mindset, ensuring we continue to uphold our brand promise of trust, security, and service.

As part of Team Amex, you'll experience our powerful backing with comprehensive support for your holistic well-being and many opportunities to learn new skills, develop as a leader, and grow your career. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express.

We back you with benefits that support your holistic well-being so you can be and deliver your best. This means caring for you and your loved ones' physical, financial, and mental health, as well as providing the flexibility you need to thrive personally and professionally:

  • Competitive base salaries
  • Bonus incentives
  • 6% Company Match on retirement savings plan
  • Free financial coaching and financial well-being support
  • Comprehensive medical, dental, vision, life insurance, and disability benefits
  • Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need
  • 20+ weeks paid parental leave for all parents, regardless of gender, offered for pregnancy, adoption or surrogacy
  • Free access to global on-site wellness centers staffed with nurses and doctors (depending on location)
  • Free and confidential counseling support through our Healthy Minds program
  • Career development and training opportunities

For a full list of Team Amex benefits, visit our Colleague Benefits Site.

American Express is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law. American Express will consider for employment all qualified applicants, including those with arrest or conviction records, in accordance with the requirements of applicable state and local laws, including the California Fair Chance Act, the Los Angeles County Fair Chance Ordinance for Employers, and the City of Los Angeles' Fair Chance Initiative for Hiring Ordinance. For positions covered by federal and/or state banking regulations, American Express will comply with such regulations as it relates to the consideration of applicants with criminal convictions.

We back our colleagues with the support they need to thrive, professionally and personally. That's why we have Amex Flex, our enterprise working model that provides greater flexibility to colleagues while ensuring we preserve the important aspects of our unique in-person culture. Depending on role and business needs, colleagues will either work onsite, in a hybrid model (combination of in-office and virtual days) or fully virtually.

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The below represents the expected salary range for this job requisition. Ultimately, in determining your pay, we'll consider your location, experience, and other job-related factors.

Minimum Qualifications 

  • Must have earned a Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, or another technical field before the full-time start date. 
  •   Students must have a graduation date between December 2026 and June 2027 
  • Experience developing products or projects in an academic, personal, internship, research, open-source, hackathon, or professional setting. 
  • Exposure to backend engineering, frontend engineering, cloud engineering, mobile engineering, AI / machine learning, data engineering, API development, or full-stack product development. 
  • Familiarity with Java, JavaScript, React, TypeScript, Python, C#, Go, Kotlin, Node.js, REST APIs, SQL, Spring, or similar frameworks and tools. 
  • Exposure to cloud-native development, microservices, containerization, CI/CD, DevOps, automated testing, accessibility, or secure software development concepts. 
  • Interest in AI-powered development tools, generative AI, intelligent automation, responsible AI, or machine learning fundamentals. 

Preferred Qualifications 

  • Programming experience through coursework, projects, research, internships, open-source contributions, hackathons, or extracurricular activities using one or more languages such as Java, JavaScript, Python, C#, Go, Kotlin, Node.js, or similar technologies. 
  • Interest in building software applications across web, mobile, backend, cloud, API, microservices, or full-stack environments. 
  • Exposure to software development across backend, frontend, cloud, mobile, API, data, or full-stack environments. 
  • Exposure to advanced AI software engineering concepts such as LLM integrations, prompt engineering, prompt evaluation, embeddings, retrieval-augmented generation, vector search, AI agents, agentic workflows, or human-in-the-loop review. 
  • Familiarity with building or integrating AI-enabled applications using APIs, data pipelines, model inference endpoints, evaluation workflows, or cloud-based AI services. 
  • Awareness of responsible AI practices, including model reliability, explainability, privacy, security, governance, bias mitigation, guardrails, and validation of AI-generated outputs. 
  • Experience with modern software development practices such as version control, testing, code reviews, Agile methodologies, and collaborative development. 
  • Curiosity for emerging technologies, including artificial intelligence, machine learning, developer productivity tooling, automation, and responsible AI practices. 
  • Strong communication, teamwork, collaboration, learning agility, and the ability to learn emerging technologies 

Technology Areas and Preferred Skills 

American Express software engineers may be aligned to different technology teams based on business needs, project requirements, and individual strengths. Experience in one or more of the following areas is beneficial: 

  • Back-End Engineering: Java, Python, Go, APIs, microservices, distributed systems, Big Data, or data-oriented engineering concepts. 
  • Front-End Engineering: JavaScript, React, TypeScript, REST APIs, accessibility, and user experience principles. 
  • Cloud Engineering: Cloud-native development, microservices, CI/CD, containerization, DevOps practices, and infrastructure-aware engineering. 
  • AI & Machine Learning Engineering: Python, Java, machine learning fundamentals, AI-powered developer tools, data processing, automation, responsible AI concepts, and agentic workflow awareness. 
  • Mobile Engineering: Swift, Kotlin, API integration, mobile testing frameworks, mobile architecture, and UI/UX fundamentals. 
  • Core Skills Across All Areas: Problem solving, computer science fundamentals, communication, collaboration, curiosity, secure engineering, and a growth mindset. 

Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions. 

What Type Of Work Can You Expect? 

  • Develop, test, and improve software applications as part of an Agile scrum team. 
  • Write clean, maintainable code; participate in code reviews; and create unit tests with guidance from experienced engineers. 
  • Identify opportunities to apply new technologies, automation, and engineering practices to solve real business challenges. 
  • Partner with Product Managers, Senior Engineers, Quality Engineers, Architects, and business partners to understand requirements, prioritize features, and deliver value. 
  • Work across different parts of the technology stack, such as APIs, services, microservices, user interfaces, mobile experiences, cloud platforms, or data-driven solutions. 
  • Explore responsible use of AI-powered developer tools, machine learning concepts, and intelligent automation relevant to your team's work. 
  • Participate in graduate program learning, mentorship, networking, community, and career-development experiences while building your full-time career at American Express. 

What You'll Learn 

  • How software is built and delivered "the Amex Way," including structured onboarding, developer bootcamp concepts, code quality, testing, and secure engineering practices. 
  • How Product, Engineering, Quality, Security, and business partners collaborate from idea to production. 
  • How to translate customer or business needs into technical solutions while balancing quality, resilience, usability, and risk. 
  • How to communicate progress, ask effective questions, and share technical work with both technical and non-technical partners. 
  • How to grow through mentorship, feedback, technical learning, leadership exposure, and a strong graduate cohort community. 

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