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Ai Model Jobs in Missouri (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:

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:

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:

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:

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:

AI Agentic Tester

Denver, MO ยท On-site +1

AI Agentic Tester Denver, MO (Remote) Must-Have Skills AI Agentic Testing Functional Testing Generative AI Testing Large Language Models (LLMs) AI Agents & Agentic AI Retrieval-Augmented Generation ...

AI Agentic Tester

Denver, MO ยท On-site +1

Large Language Models (LLMs) * AI Agents & Agentic AI * Retrieval-Augmented Generation (RAG) * Prompt Engineering Validation * AI Model Validation & Evaluation * API Testing (Postman, REST APIs ...

AI Engineer

Saint Louis, MO ยท On-site

$50K - $112K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

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 ...

AI Engineer

Kansas City, MO ยท On-site

$50K - $112K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

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 ...

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Ai Model information

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 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 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.

How to get into AI modeling?

To become an AI modeler, develop strong skills in programming languages like Python, learn machine learning frameworks such as TensorFlow or PyTorch, and gain experience with data preprocessing and model training. A background in computer science, mathematics, or related fields, along with relevant certifications or courses, can also improve your prospects.

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

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

What cities in Missouri are hiring for Ai Model jobs?

Cities in Missouri with the most Ai Model job openings:

Infographic showing various Ai Model job openings in Missouri as of August 2026, with employment types broken down into 2% Internship, 72% Full Time, 10% Part Time, and 16% Contract. Highlights an 78% In-person, and 22% Remote job distribution.

Senior Software Engineer - Local AI

Jobtailor

California, MO โ€ข On-site

$140 - $210/hr

Other

Posted yesterday

New


Job description


  • Partnering with NVIDIA software, research, architecture, and product teams to align strategies and technical needs for fostering the ecosystem of AI on RTX and DGX PCs.

  • Collaborate closely with industry partners to advance AI across critical domainsโ€”including graphics, web browsers, and edge devicesโ€”by driving innovation in both open and closed source technologies with emphasis on system level support.

  • Improving performance on current and next-generation GPU architectures by conducting in-depth analysis and end-to-end optimization of AI models, data processing pipelines, and inference runtime features.

  • Identifying, evaluating, and implementing compute and memory optimization techniquesโ€”such as quantization, distillation, and pruningโ€”for large AI models; fine-tuning and compressing models to fit edge devices.


Requirements

  • Bachelor's, Master's, or PhD in Computer Science, Software Engineering, Mathematics, or a related field (or equivalent experience).

  • Excellent C++ programming and debugging skills with a strong understanding of data structures and algorithms.

  • 5+ years of experience with proficiency in AI inferencing pipelines and applications using ML/DL frameworks, including ONNX RT, PyTorch, Tensor RT, llama.cpp and vLLM.

  • Strong analytical and problem-solving abilities, with the ability to multitask effectively in a dynamic environment.

  • Outstanding written and oral communication skills enabling effective collaboration with management and engineering teams.


Core Competencies

Demonstrates expertise in AI model optimization and performance enhancement on GPU architectures, with strong proficiency in C++ programming and ML/DL frameworks. Capable of collaborating effectively with cross-functional teams to drive innovation in AI technologies.


Highest-signal resume keywords

  • C++ Programming

  • AI Inferencing Pipelines

  • ML/DL Frameworks

  • Performance Optimization

  • Data Structures and Algorithms


ATS Optimization Keywords
Hard Skills

  • C++ Programming

  • Data Structures

  • Algorithms

  • AI Model Optimization

  • Quantization

  • Distillation

  • Pruning

  • End-to-End Optimization

  • Inference Runtime Features

  • Multitasking


Soft Skills

  • Analytical Abilities

  • Problem-Solving

  • Written Communication

  • Oral Communication

  • Collaboration


Industry Keywords

  • AI Ecosystem

  • GPU Architectures

  • Edge Devices

  • Open Source Technologies

  • Closed Source Technologies


Tools & Technologies

  • ONNX RT

  • PyTorch

  • Tensor RT

  • Llama.cpp

  • VLLM

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