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Ai Model Jobs in Arizona (NOW HIRING)

Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required -- your domain knowledge is what matters. Key Responsibilities:

Provide detailed feedback to improve AI model performance and financial reasoning. Required Skills * Critical Thinking * Analytical Reasoning * Quality Assurance * Prompt Engineering * AI Output ...

Job Overview The Model Manager is responsible for overseeing a portfolio of complex and strategically important AI-driven supervisory models, ensuring their performance, governance compliance ...

Manager - Application & AI Security

Scottsdale, AZ · On-site

$58.75 - $78.25/hr

Lead application threat modeling and secure-code development practices. * Establish and maintain ... AI Security & Governance * Serve as the technical owner for enterprise AI security controls and AI ...

Lead Gen AI Engineer

Phoenix, AZ · On-site

$101K - $134K/yr

... Models (LLMs) into production environments. This is a hands-on technical leadership role deep knowledge of AI architecture, data preparation, prompt engineering, vector databases, and modern AI ...

... Models (LLMs) into production environments. This is a hands-on technical leadership role requiring deep knowledge of AI architecture, data preparation, prompt engineering, vector databases, and ...

AI Engineer

Phoenix, AZ · On-site

$50K - $112K/yr

Your work will involve designing AI systems, data wrangling, and software implementation to enable the AI models to be useful and scalable. As an Associate, you will focus on learning and ...

Lead Gen AI Engineer with Python

Phoenix, AZ · On-site

$139K - $170K/yr

... Models (LLMs) into production environments. This is a hands-on technical leadership role requiring deep knowledge of AI architecture, data preparation, prompt engineering, vector databases, and ...

Showing results 21-40

Ai Model information

What is an AI model?

AI models are computer programs designed to simulate human intelligence by learning patterns from data and making predictions or decisions based on that learning. These models can perform a variety of tasks, such as recognizing speech, translating languages, analyzing images, and generating text. AI models are created using machine learning algorithms and are trained on large datasets to improve their accuracy and performance. Popular examples include neural networks, decision trees, and support vector machines. The effectiveness of an AI model depends on the quality of the data, the chosen algorithm, and the training process.

What are the key skills and qualifications needed to thrive as an AI model, and why are they important?

To excel as an AI Model Developer, you need strong programming skills (especially in Python), a solid understanding of machine learning algorithms, and typically a degree in computer science, data science, or a related field. Familiarity with ML frameworks like TensorFlow or PyTorch, cloud platforms, and relevant certifications such as TensorFlow Developer or AWS Machine Learning Specialty are valuable. Critical thinking, continuous learning, and effective collaboration with interdisciplinary teams are key soft skills for success. These competencies enable the creation of accurate, reliable AI models that can effectively solve complex real-world problems.

What are some common challenges faced by professionals working as AI model developers, and how can they address them?

Professionals working as AI Model developers often encounter challenges such as managing large and complex datasets, ensuring model accuracy, and addressing issues of bias in algorithms. They may also need to balance the trade-off between model performance and interpretability, especially when deploying models in production environments. To overcome these challenges, AI Model developers typically collaborate closely with data engineers, domain experts, and other stakeholders, regularly validate their models, and stay updated with the latest advancements in the field to adopt best practices.

What is the difference between Ai Model vs Data Scientist?

AspectAi ModelData Scientist
Required CredentialsKnowledge of machine learning, programming skills, sometimes certifications in AI/MLDegree in data science, statistics, computer science; certifications beneficial
Work EnvironmentFocus on developing, training, and deploying AI modelsData analysis, interpretation, and visualization; often collaborates with AI teams
Industry UsageUsed in AI development, automation, and predictive modelingApplied across industries for insights, reporting, and decision-making

While both roles involve working with data and algorithms, an Ai Model primarily focuses on creating and refining AI systems, whereas a Data Scientist analyzes data to generate insights and supports AI development. The roles often overlap but serve distinct functions within the data and AI ecosystem.

What is the easiest AI Model job to get into?

Entry-level AI model jobs often include roles such as data annotator or junior machine learning assistant, which typically require basic programming skills in Python and understanding of data labeling. These positions usually have lower experience requirements and may offer on-the-job training, making them accessible for beginners entering the AI field.

What are popular job titles related to Ai Model jobs in Arizona?

For Ai Model jobs in Arizona, the most frequently searched job titles are:

What cities in Arizona are hiring for Ai Model jobs?

Cities in Arizona with the most Ai Model job openings:

Infographic showing various Ai Model job openings in Arizona as of August 2026, with employment types broken down into 70% Full Time, 16% Part Time, and 14% Contract. Highlights an 93% In-person, and 7% Remote job distribution.

AI Trainer - Microbiology Expert

micro1 AI

Phoenix, AZ • Remote

$70 - $90/hr

Part-time

Posted 21 days ago


Job description

Role Title: Microbiologist


Role Type: Contractor


Location: Remote


micro1 is engaging Microbiologists to contribute their scientific expertise to a unique customer project. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Key Responsibilities:

  1. Investigate and analyze the development, morphology, and behavior of microscopic organisms including bacteria, fungi, and algae.
  2. Contribute to the study of the relationship between microorganisms and disease, supporting projects involving medical microbiology.
  3. Assess the impact of antibiotics and other agents on microbial populations, providing insights for AI model accuracy.
  4. Document experimental findings and processes with a focus on clarity for AI training data.
  5. Collaborate with interdisciplinary teams to ensure scientific rigor and data integrity in AI development.
  6. Provide written and verbal expertise on microbiological phenomena and their relevance to real-world and computational contexts.
  7. Utilize rubrics and established evaluation criteria to assess data quality and support AI training workflows.


Required Skills and Qualifications:

  1. Bachelor’s degree or higher in Biology, Microbiology, Chemistry, or a related field.
  2. Extensive knowledge of bacterial, fungal, and algal systems.
  3. Demonstrated expertise in investigating microbial structure and physiology.
  4. Strong written and verbal communication skills for technical and interdisciplinary collaboration.
  5. Ability to document processes and findings clearly for integration into AI systems.
  6. Comfort working independently in a fully remote, digital-first environment.
  7. Attention to detail and commitment to scientific accuracy.


Preferred Qualifications:

  1. Prior experience developing or applying rubrics in scientific or educational contexts.
  2. Experience with AI, machine learning, or annotation projects related to biology or microbiology.
  3. Advanced degree (Master’s or PhD) in a relevant field.