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Artificial Intelligence Software Developer Jobs in Dallas, TX

Senior Software Engineer

Addison, TX · On-site

$118K - $156K/yr

... artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google ...

Collaborate with software engineers, product managers, and designers to deliver AI features. * Stay ... Artificial Intelligence

Demonstrated proficiency in artificial intelligence concepts, with hands-on experience using AI ... Our software engineering team is responsible for building the core databases, application ...

We are looking for passionate Software Engineering Interns with a strong interest in Artificial Intelligence to work on the development of our next-generation, AI-driven applications for our global ...

We are looking for passionate Software Engineering Interns with a strong interest in Artificial Intelligence to work on the development of our next-generation, AI-driven applications for our global ...

We are looking for passionate Software Engineering Interns with a strong interest in Artificial Intelligence to work on the development of our next-generation, AI-driven applications for our global ...

Showing results 41-60

Artificial Intelligence Software Developer information

See Dallas, TX salary details

$47.5K

$110.7K

$164.3K

How much do artificial intelligence software developer jobs pay per year?

As of Sep 11, 2026, the average yearly pay for artificial intelligence software developer in Dallas, TX is $110,688.00, according to ZipRecruiter salary data. Most workers in this role earn between $89,100.00 and $128,700.00 per year, depending on experience, location, and employer.

What is an artificial intelligence software developer?

An Artificial Intelligence (AI) Software Developer is a technology professional who designs, builds, and maintains software applications that utilize AI and machine learning techniques. They work with algorithms, data processing, and neural networks to create intelligent systems capable of tasks such as image recognition, language processing, and decision-making. These developers often collaborate with data scientists and engineers to integrate AI models into products and services, continually improving their accuracy and efficiency. Their work is fundamental in creating smarter applications across industries like healthcare, finance, and e-commerce.

What are the key skills and qualifications needed to thrive as an artificial intelligence software developer, and why are they important?

To thrive as an Artificial Intelligence Software Developer, you need strong programming skills (especially in Python, Java, or C++), a solid background in mathematics and statistics, and typically a degree in computer science or a related field. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), cloud computing platforms, and relevant certifications like Google Cloud Professional Machine Learning Engineer are highly beneficial. Critical thinking, creativity, and effective collaboration are standout soft skills for this role. These competencies enable the development of innovative AI solutions, efficient problem-solving, and successful teamwork on complex projects.

What are some common challenges faced by artificial intelligence software developers when transitioning machine learning models from development to production?

Artificial Intelligence Software Developers often encounter challenges when moving machine learning models from the development environment to production, such as ensuring scalability, managing data inconsistencies, and maintaining model performance over time. Integrating models into existing systems may require collaboration with DevOps and data engineering teams to address deployment pipelines, monitoring, and version control. It's also important to implement robust testing and continuous evaluation processes to catch data drift or performance degradation. Overcoming these challenges requires strong communication skills and an understanding of both AI algorithms and software engineering best practices.

What is the difference between Artificial Intelligence Software Developer vs Machine Learning Engineer?

AspectArtificial Intelligence Software DeveloperMachine Learning Engineer
Required CredentialsBachelor's in CS, AI, or related; programming skillsBachelor's in CS, Data Science, or related; strong math and programming skills
Work EnvironmentSoftware development teams, AI projects, R&DData-focused teams, model development, deployment
Employer & Industry UsageTech companies, research labs, startupsTech firms, finance, healthcare, research institutions
Common Search & ComparisonYesYes

Artificial Intelligence Software Developers design and implement AI applications, focusing on integrating AI algorithms into software solutions. Machine Learning Engineers specialize in developing and deploying machine learning models, often working with large datasets. While both roles require programming skills and knowledge of AI concepts, AI Developers focus on broader AI system integration, whereas ML Engineers concentrate on model training and optimization.

What cities near Dallas, TX are hiring for Artificial Intelligence Software Developer jobs?

Cities near Dallas, TX with the most Artificial Intelligence Software Developer job openings:

Infographic showing various Artificial Intelligence Software Developer job openings in Dallas, TX as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 9% Part Time, and 4% Contract. Highlights an 88% Physical, 4% Hybrid, and 8% Remote job distribution, with an average salary of $110,641 per year, or $53.2 per hour.

Senior Software Engineer

Addison, TX • On-site

$118K - $156K/yr

Other

Posted 9 days ago


Job description

MINIMUM QUALIFICATIONS:
  • Bachelor's degree or equivalent practical experience.
  • 5 years of experience in object-oriented programming with Java, C++, or Python.
  • 3 years of experience testing, maintaining, or launching software products.
  • 1 year of experience with software design and architecture.
PREFERRED QUALIFICATIONS:
  • Master's degree or PhD in Computer Science or related technical field.
  • 5 years of experience with data structures and algorithms.
  • 1 year of experience in a technical leadership role.
  • Experience developing accessible technologies.
ABOUT THE JOB:

Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google’s needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.

With your technical expertise, you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google
[https://www.google.com/about/careers/applications/benefits/].

RESPONSIBILITIES:
  • Write and test product or system development code.
  • Participate in, or lead, design reviews with peers and stakeholders to decide amongst available technologies.
  • Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
  • Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
  • Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
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