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Artificial Neural Network Jobs (NOW HIRING)

Urnet Inc

Engineer

Houston, TX · On-site

$100K - $150K/yr

Strong understanding of Artificial Neural Networks and their applications in systems engineering ... network solutions into existing systems. * Conduct system testing and validation to ensure ...

Epsilon Systems Solutions

$121K - $167K/yr

Artificial neural network training * Programming: Python, C++, MATLAB, Go, PyTorch, TensorFlow, Keras * Image processing * Building graph databases * Building PostgreSQL databases with vector store ...

Cmu

RF Cyber Lab Intern

Pittsburgh, PA · On-site

$14.50 - $19.50/hr

Artificial neural networks design and implementation * Spiking temporal neural network implementation * Strong programming ability and analytical skills Job Function Breakdown: 70% Development and ...

Carnegie Mellon University

RF Cyber Lab Intern

Pittsburgh, PA · On-site

$14 - $18.75/hr

Artificial neural networks design and implementation * Spiking temporal neural network implementation * Strong programming ability and analytical skills Job Function Breakdown: 70% Development and ...

Apex Informatics

Data Scientist

Pleasanton, CA · On-site

... tree learning, artificial neural networks, etc.) and their real-world advantages drawbacks ... GLM Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc ...

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Artificial Neural Network information

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How much do artificial neural network jobs pay per year?

As of Jun 9, 2026, the average yearly pay for artificial neural network in the United States is $106,570.00, according to ZipRecruiter salary data. Most workers in this role earn between $80,500.00 and $128,000.00 per year, depending on experience, location, and employer.

What is the difference between Artificial Neural Network vs Data Scientist?

AspectArtificial Neural NetworkData Scientist
Required CredentialsKnowledge of machine learning, programming, mathematicsDegree in statistics, computer science, or related field; often includes certifications
Work EnvironmentResearch labs, tech companies, AI development teamsData analysis, modeling, business insights in various industries
Industry UsageAI, machine learning, deep learning projectsData analysis, predictive modeling, decision support

Artificial Neural Networks are algorithms used within machine learning to model complex patterns, while Data Scientists analyze data to extract insights and build models, often utilizing neural networks as tools. Both roles require strong technical skills but focus on different aspects of data and AI development.

What are some common challenges faced when working as an Artificial Neural Network Engineer?

As an Artificial Neural Network Engineer, one common challenge is managing the balance between model complexity and computational efficiency, especially when deploying models in real-world applications. Ensuring high-quality, well-labeled data is another hurdle, as the performance of neural networks heavily depends on the data used for training. Additionally, keeping up with rapidly evolving frameworks and research can be demanding but is essential for staying competitive in the field. Collaboration with data scientists, domain experts, and software engineers is frequent, requiring strong communication skills to translate model outputs into actionable business insights.

What are the key skills and qualifications needed to thrive as an Artificial Neural Network Engineer, and why are they important?

To thrive as an Artificial Neural Network Engineer, you need a strong background in mathematics, computer science, and machine learning, often supported by a relevant degree and experience in AI research or development. Proficiency with programming languages such as Python, frameworks like TensorFlow or PyTorch, and understanding of deep learning architectures is essential. Analytical thinking, problem-solving, and effective communication are vital soft skills for innovating and collaborating within multidisciplinary teams. These skills ensure the accurate design, implementation, and optimization of neural network models for solving complex real-world problems.

What are Artificial Neural Networks?

Artificial Neural Networks (ANNs) are computing systems inspired by the biological neural networks that make up animal brains. They consist of interconnected nodes (or 'neurons') organized in layers, which process data by learning patterns from examples. ANNs are the foundation of many modern AI applications, such as image recognition, natural language processing, and predictive analytics. By adjusting connections and weights based on input data, ANNs can learn to perform complex tasks without being explicitly programmed.
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Artificial Neural Network jobs near you

Engineer

Urnet Inc

Houston, TX • On-site

$100K - $150K/yr

Full-time

Posted 12 days ago

Job description

About the Role:

As an Engineer in the Information industry, you will play a pivotal role in designing and implementing advanced systems that leverage Artificial Neural Networks to solve complex problems. Your work will directly contribute to the development of innovative solutions that enhance operational efficiency and drive technological advancements. You will collaborate with cross-functional teams to ensure that systems are robust, scalable, and meet the highest standards of quality. This position requires a deep understanding of C++ programming to develop and optimize algorithms that power our neural network models. Ultimately, your contributions will help shape the future of our technology offerings and improve user experiences across various applications.

Minimum Qualifications:

  • Bachelor's degree in Engineering, Computer Science, or a related field.
  • Proven experience in developing applications using C++.
  • Strong understanding of Artificial Neural Networks and their applications in systems engineering.

Preferred Qualifications:

  • Master's degree in a relevant field.
  • Experience with machine learning frameworks such as TensorFlow or PyTorch.
  • Familiarity with software development methodologies and tools.

Responsibilities:

  • Design, develop, and implement algorithms using Artificial Neural Networks to address specific engineering challenges.
  • Write and maintain high-quality C++ code to support system functionalities and performance optimization.
  • Collaborate with software developers, data scientists, and other engineers to integrate neural network solutions into existing systems.
  • Conduct system testing and validation to ensure reliability and accuracy of the implemented solutions.
  • Document technical specifications and provide training to team members on new technologies and methodologies.

Skills:

The required skills in Artificial Neural Networks will be utilized daily to create models that can learn from data and improve over time, ensuring that our systems remain cutting-edge. Proficiency in C++ is essential for writing efficient code that can handle complex computations and large datasets, which is a core part of our engineering processes. Systems engineering skills will be applied to integrate various components of our technology stack, ensuring seamless operation and performance. Additionally, preferred skills such as experience with machine learning frameworks will enhance your ability to implement state-of-the-art solutions. Overall, these skills will enable you to contribute effectively to projects that require innovative thinking and technical expertise.