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Learning Engineer Jobs in Arizona (NOW HIRING)

* 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 ...

We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... The Search and Discovery ML team at Instacart works alongside world-class engineers, data ...

We use machine learning and Internet-scale data to elevate customer experience, improve efficiency ... The Search and Discovery ML team at Instacart works alongside world-class engineers, data ...

Showing results 21-40

Learning Engineer information

See Arizona salary details

$35.4K

$108K

$178.5K

How much do learning engineer jobs pay per year?

As of Sep 7, 2026, the average yearly pay for learning engineer in Arizona is $107,973.00, according to ZipRecruiter salary data. Most workers in this role earn between $77,300.00 and $141,200.00 per year, depending on experience, location, and employer.

What is a learning engineer?

A Learning Engineer is a professional who designs, develops, and implements educational experiences using principles from learning science, technology, and instructional design. They work to create effective learning environments, often integrating digital tools and data analytics to enhance teaching and learning outcomes. Learning Engineers collaborate with educators, subject matter experts, and technologists to build solutions that address specific educational challenges.

What are the key skills and qualifications needed to thrive as a learning engineer?

To thrive as a Learning Engineer, you need expertise in instructional design, learning science, and educational technology, often supported by a degree in education, instructional design, or a related field. Familiarity with learning management systems (LMS), authoring tools like Articulate or Adobe Captivate, and data analytics platforms is typically required. Strong collaboration, problem-solving, and communication skills distinguish top performers in this role. These competencies are crucial for designing effective, scalable learning experiences that meet diverse learner needs and organizational goals.

How do learning engineers typically collaborate with subject matter experts and instructional designers during course development?

Learning Engineers play a pivotal role in bridging technical solutions and educational goals. They often work closely with subject matter experts to deeply understand the content, ensuring its accurate representation in digital formats. Collaboration with instructional designers is essential, as Learning Engineers translate pedagogical strategies into interactive and accessible learning experiences, utilizing technologies such as learning management systems, analytics, and multimedia tools. Effective communication and iterative feedback are key, as these teams work together to design, test, and refine educational products that maximize learner engagement and success.

What is the difference between Learning Engineer vs Instructional Designer?

AspectLearning EngineerInstructional Designer
Required CredentialsBachelor's or master's in education, instructional design, or related fields; familiarity with e-learning toolsBachelor's or master's in education, instructional design, or related fields; expertise in curriculum development
Work EnvironmentCollaborates with developers, data analysts, and educators to build digital learning solutionsDesigns and develops educational content and curricula for various learning settings
Employer & Industry UsageTech companies, online education platforms, corporate trainingSchools, universities, corporate training departments

Learning Engineers focus on developing and implementing innovative digital learning solutions using technology and data analysis, while Instructional Designers primarily create educational content and curricula. Both roles require similar educational backgrounds and often work in overlapping industries, but their core responsibilities differ in approach and focus.

Are machine learning engineers still in demand?

Machine learning engineers remain in high demand due to the growing adoption of AI and data-driven solutions across industries. They typically require skills in programming, data analysis, and familiarity with tools like TensorFlow or PyTorch, and often hold advanced degrees in related fields. The role is expected to continue growing as organizations prioritize automation and intelligent systems.

What does a learning engineer do?

A learning engineer designs, develops, and implements educational programs and digital learning solutions. They analyze learning needs, create instructional content, and often use tools like learning management systems (LMS) to enhance training effectiveness. Strong skills in instructional design, technology, and data analysis are essential for this role.

What cities in Arizona are hiring for Learning Engineer jobs?

Cities in Arizona with the most Learning Engineer job openings:

Infographic showing various Learning Engineer job openings in Arizona as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 20% Part Time, 2% Temporary, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $107,973 per year, or $51.9 per hour.

Full-time

Re-posted 21 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