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Fairness Machine Learning Internship Jobs (NOW HIRING)

Apex Informatics

Machine Learning Engineer

Applied Machine Learning Engineer | Music Software (Multiple Roles open) Role: Applied Machine ... fairness and responsible AI use in music-related applications. What You Bring • Proven software ...

Elder Research

Machine Learning Engineer

Richmond, VA · On-site

Machine Learning Engineer Elder Research Inc., a wholly owned subsidiary of MANTECH international ... Ensure AI systems adhere to ethical guidelines, transparency, and fairness principles. Solid ...

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Elder Research

Machine Learning Engineer

Manhattan, NY · On-site

Machine Learning Engineer Elder Research Inc., a wholly owned subsidiary of MANTECH international ... Ensure AI systems adhere to ethical guidelines, transparency, and fairness principles. Solid ...

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Fairness Machine Learning Internship information

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

As of Jun 5, 2026, the average yearly pay for fairness machine learning internship in the United States is $42,584.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,500.00 and $46,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Fairness Machine Learning Intern, and why are they important?

To thrive as a Fairness Machine Learning Intern, you typically need a strong background in machine learning concepts, statistics, and programming languages such as Python, along with coursework or experience in algorithmic fairness or ethics. Familiarity with ML frameworks like TensorFlow or PyTorch, as well as tools for fairness evaluation and analysis, is often required. Critical thinking, attention to detail, and effective communication are important soft skills for analyzing biases and presenting findings. These skills are crucial to ensure the development of fair, transparent, and responsible machine learning models in real-world applications.

What types of projects do Fairness Machine Learning Interns typically work on, and how do these contribute to larger organizational goals?

Fairness Machine Learning Interns often work on projects that involve analyzing machine learning models for biases, developing metrics to evaluate fairness, and proposing algorithmic improvements to enhance equity in model outcomes. These projects may include auditing datasets for representativeness, testing models across diverse demographic groups, and collaborating with data scientists and ethicists to implement fairer solutions. The work directly supports the organization's commitment to responsible AI by ensuring products and services are inclusive and unbiased. Interns often present their findings to cross-functional teams, gaining exposure to both technical and ethical aspects of machine learning.

What is a Fairness Machine Learning Internship?

A Fairness Machine Learning Internship is a specialized role where interns work on developing and evaluating machine learning models to ensure they are fair, unbiased, and equitable. Interns typically assist in researching, designing, and implementing techniques that identify and mitigate biases in data and algorithms. The goal is to create models that make decisions without favoring or discriminating against any group based on sensitive attributes such as race, gender, or age. This internship provides hands-on experience in ethical AI development and often involves collaboration with data scientists, engineers, and ethicists.

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

AspectFairness Machine Learning InternshipData Science Internship
Required CredentialsRelevant coursework in machine learning, statistics, ethicsDegree in data science, statistics, computer science
Work EnvironmentResearch-focused, tech companies, AI labsBusiness analytics, tech firms, consulting
Employer & Industry UsageAI ethics, fairness, responsible AI projectsData analysis, predictive modeling, business insights

Fairness Machine Learning Internships focus on ethical AI, bias mitigation, and responsible algorithms, often within research or AI development teams. Data Science Internships cover broader data analysis, modeling, and insights across various industries. While both roles require strong technical skills, fairness internships emphasize ethics and fairness in AI, making them more specialized.

Infographic showing various Fairness Machine Learning Internship job openings in the United States as of May 2026, with employment types broken down into 3% As Needed, 11% Full Time, 83% Part Time, and 3% Contract. Highlights an 94% Physical, 1% Hybrid, and 5% Remote job distribution, with an average salary of $42,584 per year, or $20.5 per hour.
Machine Learning Engineer

Machine Learning Engineer

Apex Informatics

Remote

Other

Posted 8 days ago

Job description

Applied Machine Learning Engineer | Music Software (Multiple Roles open)
Role: Applied Machine Learning Engineer (Mid - Senior Opportunity) Company: Splash
Employment Type: Contract (3 months +, potential for extension) Location: Remote
We are seeking an Applied Machine Learning Engineer with a strong focus on practical solutions and software development (ability to work on both open-ended research problems and production-ready API code). In this role, you'll leverage off-the-shelf tools and custom-built ML models to solve challenges in music product development and improve manual music processes. This position is ideal for engineers with demonstrable experience building functional, production-ready models and who are passionate about user experience and Product.
Key Responsibilities:
• Design and implement ML algorithms to enhance music creation tools and solve various user problems in line with product goals.
• Identify and implement off-the-shelf ML and AI tools to solve practical problems efficiently.
• Understand the requirements of running models in production, including domain shift testing, QA, A/B testing and so on.
• Maintain production-ready code with considerations for how solutions fit the product and enhance the user experience.
• Build scalable, maintainable data pipelines to handle audio and other unstructured data.
• Collaborate with Product and Engineering teams to ensure seamless integration of ML solutions into production systems.
• Evaluate, deploy, and fine-tune pre-trained models for tasks like audio analysis, melody generation, and process automation.
• Uphold ethical AI practices, ensuring fairness and responsible AI use in music-related applications.
What You Bring
• Proven software development experience, ideally in Python (other languages a plus).
• Experience implementing and deploying ML models, using PyTorch framework.
• Familiarity with AWS cloud environment for deploying and scaling ML solutions.
• Ability to preprocess and model unstructured data, especially audio.
• A strong focus on applied problem-solving, with a practical approach to integrating existing tools and systems.
• A good understanding of music, production, or audio technology processes (or a strong interest in music)
• Familiarity with GenAI architectures like transformers, LLMs, or diffusion models.
• Proactive nature, ability to creatively solve problems you face and bring new ideas to the team.
• Clear and effective communication with technical and non-technical stakeholders.
• Ability to work independently and remotely while collaborating closely with cross-functional teams.

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