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Machine Learning Engineer Apprenticeship Jobs in Utah

* 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 ...

* 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 ...

* 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 ...

* 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 ...

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

$40/hr

Implement tooling and features to support machine learning model development and deployment under the direction of a full-time Machine Learning Engineer * Help integrate tools such as LlamaIndex and ...

Showing results 21-40

Machine Learning Engineer Apprenticeship information

What is a machine learning engineer apprenticeship?

A Machine Learning Engineer Apprenticeship is a structured training program that combines hands-on work experience with classroom or online learning in the field of machine learning. Apprentices work under the guidance of experienced professionals to develop skills in data analysis, building machine learning models, and deploying algorithms in real-world applications. This apprenticeship is ideal for individuals seeking to enter the field of artificial intelligence without prior extensive experience, as it provides practical training and mentorship. Typically, apprenticeships last from several months to a couple of years and may lead to full-time employment upon successful completion.

What types of projects can I expect to work on during a machine learning engineer apprenticeship?

As a Machine Learning Engineer Apprentice, you can expect to participate in hands-on projects that involve data preprocessing, building and evaluating machine learning models, and collaborating with cross-functional teams such as data scientists and software engineers. Common projects may include developing recommendation systems, automating data analysis tasks, or implementing natural language processing solutions. These experiences provide valuable exposure to real-world datasets and industry-standard tools, helping you build foundational skills for a long-term career in machine learning.

What are the key skills and qualifications needed to thrive as a machine learning engineer apprentice, and why are they important?

To thrive as a Machine Learning Engineer Apprentice, a solid understanding of mathematics, programming (especially Python), and foundational machine learning concepts is essential, often supported by coursework or a degree in computer science or a related field. Familiarity with tools such as TensorFlow, PyTorch, scikit-learn, and version control systems like Git is typically required. Strong analytical thinking, attention to detail, and the ability to collaborate and communicate complex ideas clearly are valuable soft skills. These abilities are crucial for efficiently developing, testing, and deploying machine learning models while contributing effectively to team projects.

What cities in Utah are hiring for Machine Learning Engineer Apprenticeship jobs?

Cities in Utah with the most Machine Learning Engineer Apprenticeship job openings:

Infographic showing various Machine Learning Engineer Apprenticeship job openings in Utah as of June 2026, with employment types broken down into 91% Full Time, and 9% Part Time. Highlights an 84% In-person, 2% Hybrid, and 14% Remote job distribution.

Machine Learning Engineer

Salt Lake City, UT โ€ข On-site

Full-time

Re-posted 29 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