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Machine Learning Engineer Intern Jobs in Arizona

Bespoke Labs

Machine Learning Engineer

Glendale, AZ

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Universal Avionics

AI/ML Engineering Intern

Tucson, AZ · On-site

$14.50 - $18.75/hr

AI/ML Engineering Intern Tucson, AZ (Onsite) | Aerospace & Avionics 9/80 Schedule Available - Every ... Design and develop scalable AI solutions using machine learning models and tools * Build and ...

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All JobsMachine Learning Engineer Intern JobsMachine Learning Engineer Intern Jobs in Arizona

Machine Learning Engineer Intern information

See Arizona salary details

$23.8K

$39.7K

$82K

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

As of Jun 21, 2026, the average yearly pay for machine learning engineer intern in Arizona is $39,683.00, according to ZipRecruiter salary data. Most workers in this role earn between $30,300.00 and $42,900.00 per year, depending on experience, location, and employer.

What types of projects and tasks do Machine Learning Engineer Interns typically work on?

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 is a Machine Learning Engineer Intern job?

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 are the key skills and qualifications needed to thrive in the Machine Learning Engineer Intern position, and why are they important?

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 are the most commonly searched types of Machine Learning Engineer jobs in Arizona? The most popular types of Machine Learning Engineer jobs in Arizona are:
What are popular job titles related to Machine Learning Engineer Intern jobs in Arizona? For Machine Learning Engineer Intern jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Intern jobs in Arizona look for? The top searched job categories for Machine Learning Engineer Intern jobs in Arizona are:
What cities in Arizona are hiring for Machine Learning Engineer Intern jobs? Cities in Arizona with the most Machine Learning Engineer Intern job openings:
Machine Learning Engineer Intern jobs near you
Infographic showing various Machine Learning Engineer Intern job openings in Arizona as of June 2026, with employment types broken down into 2% As Needed, 89% Full Time, and 9% Part Time. Highlights an 87% Physical, 6% Hybrid, and 7% Remote job distribution, with an average salary of $39,683 per year, or $19.1 per hour.

Machine Learning Engineer

Bespoke Labs

Flagstaff, AZ

Full-time

Posted 3 days ago

Job description

About Us

We are AI researchers and builders who understand how to curate data and RL environments that truly improve models. We curated OpenThoughts, one of the best open reasoning datasets, and have trained SOTA models such as Bespoke-MiniCheck and Bespoke-MiniChart.

We are embarked on a journey to build Environments that are entire digital worlds that can be used to push the frontier of agents.

What You'll Be Working On

You will work directly with our research team on RL environment and task creation for agent training. This means designing observation spaces, action spaces, reward signals, and success criteria for new environments — and building the infrastructure that makes world-scale RL training possible. This is a high-ownership role; you will be building novel systems, not maintaining legacy ones.

Must-Have Skills

3+ years of ML engineering experience — model training, fine-tuning, or post-training pipelines in research or production

Strong Python and deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed precision)

Hands-on experience with LLM post-training — SFT, RLHF, PPO, DPO, or reward model training — and understanding of how training data quality affects model behavior

Familiarity with RL frameworks (Gymnasium, dm_env) and the ability to design or modify reward functions for agent training objectives

Experience running experiments at scale on cloud or HPC (AWS, GCP, SLURM, or Ray)

Solid understanding of evaluation methodology — held-out sets, benchmark design, avoiding train/eval contamination

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