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Machine Learning Developer Intern Jobs in Chicago, IL

Hardware Machine Learning Engineer Chicago, United States; New York, United States We are deploying machine learning directly onto custom hardware - and we want you to help drive it from the ground ...

... DevOps teams, and application teams to operationalize models reliably and securely. * Monitor ... sagemaker,machine learning,aws,python,amazon s3,aws lambda,aws step functions, Benefits ...

Machine Learning Engineer II

Chicago, IL · On-site

$100K - $137K/yr

The Machine Learning Engineer II role is part of the Technology Team, which is responsible for providing industry-leading machine learning-based tools or processes to the Company, which provide a ...

We're looking for a Principal Machine Learning Engineer to help shape the next phase of our platform - influencing architecture, driving best practices, and solving high-leverage problems. You'll ...

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Showing results 1-20

Machine Learning Developer Intern information

See Chicago, IL salary details

$26.3K

$43.9K

$90.7K

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

As of Jun 10, 2026, the average yearly pay for machine learning developer intern in Chicago, IL is $43,867.00, according to ZipRecruiter salary data. Most workers in this role earn between $33,500.00 and $47,400.00 per year, depending on experience, location, and employer.

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 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, and why are they important?

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.

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 Engineer

Senior Machine Learning Engineer

Allied Benefit Systems

Chicago, IL • Remote

$126K - $166K/yr

Full-time

Medical, Dental, Vision, Life, PTO

Posted 9 days ago


Allied Benefit Systems rating

8.1

Company rating: 8.1 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

84th of 426 rated business services


Job description

POSITION SUMMARY

The Senior Machine Learning Engineer is responsible for designing, building, and deploying scalable machine learning systems that drive business impact. This role will partner closely with data scientists, AI Technical Product Owners, and engineering teams to integrate machine learning capabilities into real business processes. The emphasis is on operational excellence, scalability, and long-term maintainability rather than research and experimentation.

ESSENTIAL FUNCTIONS

  • Design and implement end to end machine learning pipelines that support data ingestion, feature generation, model training, validation, deployment, and monitoring.
  • Operationalize models in coordination with data scientists and ensure they run reliably with requisite alerts and monitoring in production environments.
  • Build reusable frameworks and patterns that reduce friction when deploying new models or updating existing ones.
  • Ensure pipelines are secure, auditable, and appropriate for use in regulated enterprise environments.
  • Own and evolve the MLOps toolchain that supports model versioning, artifact management, experiment tracking, and deployment workflows.
  • Implement continuous integration and deployment practices for machine learning systems.
  • Establish monitoring and alerting for model performance, data quality, drift, and system health.
  • Partner with cloud and platform teams to manage compute resources, cost controls, and environment configurations.
  • Work with application engineering teams to integrate machine learning outputs into downstream systems and user workflows.
  • Support real time and batch inference patterns depending on business needs.
  • Ensure that machine learning services meet performance, reliability, and availability expectations for production use.
  • Collaborate closely with data scientists to shape models that are production ready and operationally sustainable.
  • Provide guidance on feature engineering, model packaging, and performance tradeoffs from a deployment perspective.
  • Document standards, patterns, and best practices for building and operating machine learning systems.
  • Contribute to the maturation of the organization's overall AI and ML engineering discipline.
  • Other duties as assigned

EDUCATION

  • Bachelor's degree in Computer Science, Math, Statistics, or equivalent work experiencerequired.

EXPERIENCE AND SKILLS:

  • 6+ years of strong experience building and operating machine learning systems in production environments.
  • Solid software engineering skills with Python and familiarity with modern ML frameworks such as PyTorch or TensorFlow.
  • Experience with data pipelines, workflow orchestration, and model deployment patterns.
  • Hands on experience with cloud platforms and managed ML services, with Azure, AWS, and/or Databricks experience preferred.
  • Understanding of MLOps concepts including model versioning, monitoring, testing, and lifecycle management.
  • Experience working with sensitive data in regulated industries such as healthcare or insurance is strongly preferred.
  • Ability to work cross functionally and translate between data science, engineering, and business stakeholders.

POSITION COMPETENCIES

  • Accountability
  • Analytical Problem Solving
  • Collaboration
  • Execution and Delivery
  • Quality and Risk Management
  • Systems Thinking
  • Technical/Functional Expertise

PHYSICAL DEMANDS

  • This is a standard desk role requiring extended sitting and computer work

WORK ENVIRONMENT

  • Remote

Here at Allied, we believe that great talent can thrive from anywhere. Our remote friendly culture offers flexibility and the comfort of working from home, while also ensuring you are set up for success. To support a smooth and efficient remote work experience, the internet connection must be obtained through a cable broadband or fiber optic internet service provider with speeds of at least 100Mbps download/25Mbps upload. Reliable internet service is essential for staying connected and productive.

The company has reviewed this job description to ensure that essential functions and basic duties have been included. It is not intended to be construed as an exhaustive list of all functions, responsibilities, skills, and abilities. Additional functions and requirements may be assigned by supervisors as deemed appropriate.

Compensation is not limited to base salary. Allied values our Total Rewards, and offers a competitive Benefit Package including, but not limited to, Medical, Dental, Vision, Life and Disability Insurance, Generous Paid Time Off, Tuition Reimbursement, EAP, and a Technology Stipend.

Allied reserves the right to amend, change, alter, and revise, pay ranges and benefits offerings at any time. All applicants acknowledge that by applying to the position you understand that the specific pay range is contingent upon meeting the qualification and requirements of the role, and for the successful completion of the interview selection and process. It is at the Company's discretion to determine what pay is provided to a candidate within the range associated with the role.