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Machine Learning Developer Internship Jobs (NOW HIRING)

Flexjet LLC

Machine Learning Engineer

Cleveland, OH · On-site

In this role, you will work alongside experienced engineers and data scientists to build, deploy ... Internship, academic project, or personal project experience in machine learning * Familiarity with ...

Flexjet

Machine Learning Engineer

Cleveland, OH · On-site

In this role, you will work alongside experienced engineers and data scientists to build, deploy ... Internship, academic project, or personal project experience in machine learning * Familiarity with ...

Flexjet

Machine Learning Engineer

Cleveland, OH · On-site

In this role, you will work alongside experienced engineers and data scientists to build, deploy ... Internship, academic project, or personal project experience in machine learning * Familiarity with ...

Flexjet

Machine Learning Engineer

Cleveland, OH · On-site

In this role, you will work alongside experienced engineers and data scientists to build, deploy ... Internship, academic project, or personal project experience in machine learning * Familiarity with ...

The Aerospace Corporation

Aerospace Corporation is hiring Machine Learning Engineering Interns for the Data Science and Artificial Intelligence Department (DSAID) within the Information Systems and Cyber Division (ISCD)

The Aerospace Corporation

Aerospace Corporation is hiring Machine Learning Engineering Interns for the Data Science and Artificial Intelligence Department (DSAID) within the Information Systems and Cyber Division (ISCD)

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Machine Learning Developer Internship information

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How much do machine learning developer internship jobs pay per hour?

As of Sep 15, 2026, the average hourly pay for machine learning developer internship in the United States is $22.89, according to ZipRecruiter salary data. Most workers in this role earn between $18.51 and $24.28 per hour, depending on experience, location, and employer.

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

AspectMachine Learning Developer InternshipData Scientist Internship
Required CredentialsBasic programming skills, coursework in ML/AI, some internshipsStatistics, programming, data analysis coursework, some internships
Work EnvironmentTech companies, startups, research labsTech firms, finance, healthcare, consulting
Employer & Industry UsageDevelop ML models, algorithms, software toolsAnalyze data, generate insights, build predictive models

While both internships involve working with data and algorithms, a Machine Learning Developer Internship focuses more on building and deploying machine learning models and software, whereas a Data Scientist Internship emphasizes data analysis, statistical modeling, and deriving insights from data. Candidates should choose based on their interest in either software development or data analysis within the AI and data industry.

What cities are hiring for Machine Learning Developer Internship jobs?

Cities with the most Machine Learning Developer Internship job openings:

What are the most commonly searched types of Machine Learning Developer jobs?

The most popular types of Machine Learning Developer jobs are:

What states have the most Machine Learning Developer Internship jobs?

States with the most job openings for Machine Learning Developer Internship jobs include:

What are popular job titles related to Machine Learning Developer Internship jobs?

For Machine Learning Developer Internship jobs, the most frequently searched job titles are:

Infographic showing various Machine Learning Developer Internship job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $47,621 per year, or $22.9 per hour.

Machine Learning

Manhattan, NY • On-site

Cantor Fitzgerald Securities
Finance and Insurance • 10K+ employees
Learn more about Cantor Fitzgerald Securitiesexternal

Full-time

Posted 12 days ago

Job description

Join Cantor Fitzgerald Technology Markets LLC as a Machine Learning Engineer focused on building AIdriven solutions for a highvolume financial services business. You will work closely with product, engineering, and business teams to create, test, and operationalize large language model (LLM) applications, ensuring they meet performance, reliability, and responsibleAI standards.
  • Bachelor's degree in computer science, machine learning, mathematics, physics, statistics, econometrics, or equivalent practical experience.
  • Experience contributing to production or productionlike software through work, internships, research, open source, or substantial personal projects.
  • Strong programming ability in Python with clear, tested, and maintainable code.
  • Experience with web services, data integrations, testing, logging, and basic monitoring across diverse data types.
  • Handson experience building with LLM tools or frameworks (prompting, structured outputs, toolcalling, retrieval, multistep workflows) and awareness of common failure modes.
  • Experience evaluating LLMpowered applications: building test sets, reviewing failures, defining metrics, and iterating on prompts or retrieval.
  • Solid grounding in machine learning, statistics, and experimental design with ability to interpret technical papers and documentation.
  • Strong communication skills and comfort working with product, engineering, and business partners.
  • Interest in applying AI responsibly in financial services, including privacy, security, human review, and appropriate automation.
  • Familiarity with cloud deployment, containers, and modern release pipelines.

$140,000 - $160,000

  • Design and implement LLMdriven features in production systems.
  • Build and maintain data pipelines for both structured and unstructured data.
  • Write clean, testable Python code and maintain reusable libraries.
  • Develop prompts, toolcalling workflows, and retrieval pipelines.
  • Create evaluation suites, define success metrics, and analyze failures.
  • Diagnose and mitigate hallucination, latency, and cost issues.
  • Collaborate with product, engineering, and business stakeholders.
  • Implement monitoring, logging, and alerting for AI services.
  • Contribute to responsibleAI guardrails and humanintheloop processes.
  • Document designs, experiments, and findings for internal knowledge sharing.

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