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Multimodal Learning Jobs in Arizona (NOW HIRING)

Globe Telecom, Inc.

Fullstack AI Engineer

Globe, AZ

Enables tight integration between application layers and machine learning models This role is ... multimodal inputs, and feedback loops). * Optimized Vector Databases: Efficiently indexed data ...

AZPetVet

Traveling Surgeon

Glendale, AZ

$200K - $250K/yr

Act as a resource for Lovet DVMs improving their surgical skills and/or learning new procedures to ... Confident in anesthesia utilizing up to date multimodal pain control approach for various ASA ...

AZPetVet

Traveling Surgeon

Glendale, AZ · On-site

$200K - $250K/yr

Act as a resource for Lovet DVMs improving their surgical skills and/or learning new procedures to ... Confident in anesthesia utilizing up to date multimodal pain control approach for various ASA ...

AZPetVet

Traveling Surgeon

Glendale, AZ · On-site

$200K - $250K/yr

Act as a resource for Lovet DVMs improving their surgical skills and/or learning new procedures to ... Confident in anesthesia utilizing up to date multimodal pain control approach for various ASA ...

Lovet Pet Health Care

Traveling Surgeon

Glendale, AZ

$200K - $250K/yr

Act as a resource for Lovet DVMs improving their surgical skills and/or learning new procedures to ... Confident in anesthesia utilizing up to date multimodal pain control approach for various ASA ...

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All JobsMultimodal Learning JobsMultimodal Learning Jobs in Arizona

Multimodal Learning information

What is multimodal learning?

Multimodal learning is an area of machine learning that involves integrating and processing information from multiple types of data, such as text, images, audio, and video. The goal is to create models that can understand and make predictions based on more than one data modality, similar to how humans use various senses. This approach is used in applications like speech recognition with visual cues, image captioning, and video analysis. By combining different data types, multimodal learning systems can achieve better accuracy and more robust understanding.

What is the difference between Multimodal Learning vs Data Scientist?

AspectMultimodal LearningData Scientist
Required CredentialsAdvanced degrees in AI, Machine Learning, or Computer ScienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, AI development teams, academiaBusiness, tech companies, analytics teams
Industry UsageAI research, multimedia applications, roboticsData analysis, predictive modeling, business insights

Multimodal Learning focuses on developing AI models that process and integrate multiple data types like images, text, and audio. Data Scientists analyze data to extract insights, build models, and support decision-making. While both roles involve data and algorithms, Multimodal Learning is specialized in AI model development for complex data integration, whereas Data Scientists work broadly across data analysis and interpretation.

What are the key skills and qualifications needed to thrive in multimodal learning, and why are they important?

To excel as a Multimodal Learning Specialist, you need a solid background in machine learning, data science, and computer vision, often supported by an advanced degree in a related field. Familiarity with deep learning frameworks like TensorFlow or PyTorch, experience integrating data from diverse sources (e.g., text, audio, images), and knowledge of relevant algorithms are crucial. Strong problem-solving abilities, creativity, and effective collaboration are standout soft skills for this role. These competencies are vital for developing innovative models that can process and interpret complex, multi-source data to drive impactful AI solutions.

What are some common challenges faced by professionals working in multimodal learning roles, and how can they be addressed?

Professionals in multimodal learning frequently encounter challenges related to integrating and aligning data from multiple sources, such as text, images, audio, or video. Ensuring data quality and consistency across modalities can be complex, and developing models that effectively combine heterogeneous information often requires advanced technical skills and innovative thinking. Collaboration with domain experts and other data scientists is key to overcoming these obstacles, as is staying up to date with the latest research and tools in machine learning. Regular team meetings and cross-disciplinary workshops can help foster a collaborative environment and promote knowledge sharing.
What are popular job titles related to Multimodal Learning jobs in Arizona? For Multimodal Learning jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Multimodal Learning jobs in Arizona look for? The top searched job categories for Multimodal Learning jobs in Arizona are:
What cities in Arizona are hiring for Multimodal Learning jobs? Cities in Arizona with the most Multimodal Learning job openings:
Multimodal Learning jobs near you
Infographic showing various Multimodal Learning job openings in Arizona as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 20% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Fullstack AI Engineer

Globe Telecom, Inc.

Globe, AZ

Full-time

Re-posted 18 days ago

Job description

At Globe, our goal is to create a wonderful world for our people, business, and nation. By uniting people of passion who believe they can make a difference, we are confident that we can achieve this goal.

Job Description Enables tight integration between application layers and machine learning models This role is responsible for building both the "brain" (AI models/logic) and the "body" (user interface and APIs) of an application, ensuring that AI features are not just functional but seamlessly interconnected into the user experience.

The AI Full Stack Developer operates across the full application stack, bridging model development and application production deployment, with an emphasis on scalability, integration, and usability.

  • End-to-End Development: Building responsive front-end interfaces that interact with sophisticated AI back-end, MCP Fulfilment Servers and Knowledge Management System
  • AI Integration: Implementing RAG (Retrieval-Augmented Generation) architectures and connecting LLMs to real-time data sources.
  • API Design: Creating the bridges (REST/GraphQL) that allow front-end components to communicate with ML models.
  • Product Realization: Taking a raw AI model and turning it into a polished, user-facing software product.
  • Participate in cross-functional brainstorming, strategic planning and business review sessions
  • Oversee best practices implemented during the entire DevOps/MLOps LifeCycle
  • Managed Resources like Code Repositories, API Optimization, Cloud Resources, CI/CD and Data Pipeline

NATURE OF PROBLEMS ENCOUNTERED

  • Latency Management: Solving the "slow AI" problem-ensuring users aren't left waiting while a model processes a complex request.
  • Prompt Engineering vs. UI: Aligning the "hidden" logic of an AI prompt with the visible inputs provided by a user.
  • State Management: Handling the complex states of an AI conversation (memory, context windows, and history).
  • Intermittent Failures: Debugging non-deterministic errors where an AI model provides a different (or wrong) output for the same input.

REQUIREMENTS

Technical Stack

  • Front-end: React.js, Next.js, or Vue.js; Tailwind CSS
  • Back-end: Python (FastAPI/Flask) or Node.js.
  • AI/ML: LangChain, LlamaIndex, OpenAI SDK, and basic knowledge of PyTorch/TensorFlow.
  • Databases: PostgreSQL, and Vector DBs like Pinecone, Weaviate, or Chroma.
  • Cloud Infrastructure: Google Kubernetes Engine, Cloud Functions, DFCX

Experience: 3-5 years in Full Stack Development with at least 1-2 years specifically focused on AI-integrated products.

Education: Bachelor's degree in Computer Science or Software Engineering.

More than 3-year experience on IT and Telco industry

At least 3-year experience in implementing Web/Mobile App Projects

At least 3-year experience in coordinating and managing vendors

At least 1-year experience in Testing Automation

Basic knowledge on project management discipline

Basic knowledge of DevOps Tools (CI/CD, Terraform, JIRA, Confluence)

KEY OUTPUTS

  • Application Proof of Concepts and Prototypes that will improve customer experience through AI features and capabilities
  • Functional AI Web Applications: Production-ready applications where AI is a core feature (e.g., an intelligent dashboard or an AI-driven SaaS tool).
  • Integrated API Layers: Scalable middleware that handles prompt engineering, token management, and model inference.
  • Responsive UI/UX: User interfaces specifically designed for AI interactions (e.g., streaming text responses, multimodal inputs, and feedback loops).
  • Optimized Vector Databases: Efficiently indexed data storage for fast context retrieval in AI applications.
  • Technical Documentation: Comprehensive guides for the application's architecture, including API schemas and deployment procedures.

Equal Opportunity Employer
Globe's hiring process promotes equal opportunity to applicants, Any form of discrimination is not tolerated throughout the entire employee lifecycle, including the hiring process such as in posting vacancies, selecting, and interviewing applicants.
Globe's Diversity, Equity and Inclusion Policy Commitment can be accessed here

Make Your Passion Part of Your Profession. Attracting the best and brightest Talents is pivotal to our success. If you are ready to share our purpose of Creating a Globe of Good, explore opportunities with us.

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