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Machine Learning Engineer Intern Jobs in Canton, MI

Bespoke Labs

Machine Learning Engineer

Detroit, MI · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Wayve

Machine Learning Engineer, App SW

Detroit, MI · Hybrid

$283K - $381K/yr

In order to set you up for success as a Machine Learning Engineer at Wayve, we're looking for the following skills and experience. Essential * Extensive and proven track record of shipping deep ...

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Machine Learning Engineer Intern information

See Canton, MI salary details

$23.6K

$39.5K

$81.6K

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

As of Sep 9, 2026, the average yearly pay for machine learning engineer intern in Canton, MI is $39,464.00, according to ZipRecruiter salary data. Most workers in this role earn between $30,100.00 and $42,600.00 per year, depending on experience, location, and employer.

What is a machine learning engineer intern?

A Machine Learning Engineer Intern is a temporary, entry-level role where individuals work with data scientists and engineers to develop, test, and optimize machine learning models. Interns typically assist in data preprocessing, feature engineering, model training, and evaluation. They may also work on improving existing algorithms, implementing research papers, or deploying models into production. This role provides hands-on experience with machine learning frameworks such as TensorFlow and PyTorch, as well as coding in Python and working with large datasets. The internship helps build practical skills and industry experience in artificial intelligence and data science.

What do machine learning engineer interns do?

Machine Learning Engineer Interns are often involved in data preparation, feature engineering, model development, and performance evaluation under the guidance of senior engineers or data scientists. You may help implement and test machine learning algorithms, assist in cleaning and visualizing datasets, and contribute to code reviews or research tasks. Interns frequently collaborate with cross-functional teams, such as data scientists, software engineers, and product managers, to solve real-world problems and support ongoing projects. This hands-on experience provides valuable insights into the practical application of machine learning in a professional setting.

What skills and qualifications are needed to thrive as a machine learning engineer intern?

To thrive as a Machine Learning Engineer Intern, you need a solid understanding of programming languages such as Python, knowledge of machine learning algorithms, and experience with data analysis, typically supported by coursework in computer science or related fields. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and version control systems such as Git is often required. Strong problem-solving abilities, attention to detail, and effective communication are valuable soft skills in this role. These competencies enable interns to contribute meaningfully to projects, collaborate efficiently with teams, and adapt in a fast-paced, tech-driven environment.

What job categories do people searching Machine Learning Engineer Intern jobs in Canton, MI look for?

The top searched job categories for Machine Learning Engineer Intern jobs in Canton, MI are:

What cities near Canton, MI are hiring for Machine Learning Engineer Intern jobs?

Cities near Canton, MI with the most Machine Learning Engineer Intern job openings:

Machine Learning Engineer Intern jobs near you

Machine Learning Engineer

Detroit, MI • On-site

Bespoke Labs
See more jobs at Bespoke Labsexternal

Full-time

Re-posted 24 days ago

Job description

  • Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch

  • Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment orchestration

  • Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues

  • Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy

  • Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams

  • Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics

  • Implement methods from recent ML papers quickly and turn them into production-grade systems

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