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Neural Network Jobs in Texas (NOW HIRING)

Avride

Lead AI Infrastructure Engineer

Austin, TX · On-site

$101K - $133K/yr

The vast part of the execution graph is implemented as a chain of neural network operations. The onboard mode relies on stable latencies of the inference of those networks, while in simulation we ...

Adidev Technologies Inc

DATA SCIENTIST

Austin, TX · On-site

Experience with neural network approaches to text classification CNN, RNN, LSTM,Keras * Machine Learning algorithms? Neural Networks, Naïve Bayes, Bagging & Boosting, Random Forest * Distributed ...

Adidev Technologies Inc

DATA SCIENTIST

Austin, TX · Remote

Experience with neural network approaches to text classification CNN, RNN, LSTM,Keras * Machine Learning algorithms? Neural Networks, Naïve Bayes, Bagging & Boosting, Random Forest * Distributed ...

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Neural Network information

See Texas salary details

$20.5K

$99.3K

$151.4K

How much do neural network jobs pay per year?

As of Aug 15, 2026, the average yearly pay for neural network in Texas is $99,286.00, according to ZipRecruiter salary data. Most workers in this role earn between $75,000.00 and $119,300.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a neural network engineer, and why are they important?

To thrive as a Neural Network Engineer, you need a solid background in mathematics, machine learning theory, and programming, often backed by a degree in computer science or a related field. Familiarity with frameworks like TensorFlow or PyTorch, experience in data preprocessing, and knowledge of cloud computing platforms are typically required. Strong problem-solving abilities, collaboration, and effective communication skills distinguish top professionals in this role. These competencies are vital for developing, optimizing, and deploying neural network models that drive innovation in AI-powered solutions.

What is the difference between Neural Network vs Data Scientist?

AspectNeural NetworkData Scientist
Required CredentialsKnowledge of machine learning, programming skills, often a degree in computer science or related fieldsDegree in statistics, computer science, or related fields; strong analytical skills
Work EnvironmentResearch labs, tech companies, AI development teamsBusiness environments, consulting firms, research institutions
Industry UsageDeveloping AI models, deep learning applicationsData analysis, predictive modeling, business insights

Neural networks focus on building and training AI models using complex algorithms, while data scientists analyze data to extract insights and inform decisions. Both roles often collaborate but serve different functions within the AI and data analysis ecosystem.

What is the main job of a neural network?

A neural network is a type of machine learning model used to recognize patterns and make predictions based on data. In a job context, roles involving neural networks typically focus on designing, training, and optimizing these models using programming skills and tools like Python and TensorFlow. The main job is to develop systems that can learn from data to solve complex problems such as image recognition, natural language processing, and data analysis.

What are neural networks?

Neural networks are a type of machine learning model inspired by the structure and function of the human brain. They consist of interconnected layers of nodes, or 'neurons,' that process data and learn to make predictions or decisions based on input data. Neural networks are widely used in applications such as image recognition, natural language processing, and autonomous systems. Their ability to learn complex patterns makes them powerful tools for solving problems that are difficult to program explicitly.

What are some common challenges neural network engineers face when deploying models to production environments?

Neural network engineers often encounter challenges such as model optimization for efficient inference, managing hardware constraints, and ensuring scalability during deployment. Addressing issues like latency, memory usage, and compatibility with production infrastructure is crucial, especially when models are resource-intensive. Collaborating closely with DevOps and software engineering teams is common to streamline deployment pipelines, monitor model performance, and quickly resolve issues that arise post-launch.

What are popular job titles related to Neural Network jobs in Texas?

For Neural Network jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Neural Network jobs in Texas look for?

The top searched job categories for Neural Network jobs in Texas are:

Neural Network jobs near you
Infographic showing various Neural Network job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 10% Part Time, and 6% Contract. Highlights an 92% Physical, 3% Hybrid, and 5% Remote job distribution, with an average salary of $99,286 per year, or $47.7 per hour.

Lead AI Infrastructure Engineer

Avride

Austin, TX • On-site

$101K - $133K/yr

Full-time

Posted 16 days ago

Job description

About the Team
Our team is at the core of Avride's self-driving stack. We build the base infrastructure layer that powers all autopilot code. It includes a C++ framework for implementing autonomy components, execution graph building and optimization systems, as well as runtimes that execute those graphs, both onboard and in simulation.
The vast part of the execution graph is implemented as a chain of neural network operations. The onboard mode relies on stable latencies of the inference of those networks, while in simulation we also optimize throughput at scale.
About the role
We're looking for a software engineer with a leadership mindset and deep ML infrastructure experience. You will decide and influence the ML infrastructure layer across the company. The biggest challenge we're facing at the moment is the effectiveness of GPU inference - both for onboard applications with near real-time guarantees and for offboard cases that target high throughput and deterministic execution. It is the first priority within this role.
What you'll do
  • At first, you will take on the GPU inference framework, focusing on performance
  • Later, the role assumes responsibility and ownership for broader ML infrastructure scattered across ML pipelines
  • Close collaboration with the applied ML team responsible for defining the neural model's architecture
What you'll need
  • Experience with PyTorch
  • Understanding of how GPUs work
  • Experience in diagnosing and resolving performance issues
  • Strong record of building infrastructure including distributed systems
  • 5+ years of experience with C++
  • Programming experience in multi-threaded environments - multiple processes, threads, timers, and interrupts

Candidates are required to be authorized to work in the U.S. The employer is not offering relocation, sponsorship, and remote work options are not available.
Avride is an equal opportunity employer and committed to providing reasonable accommodations to qualified applicants and employees with disabilities to ensure they have equal access to employment opportunities. Avride complies with the Americans with Disabilities Act (ADA), if you need a reasonable accommodation to assist with the application or hiring process, or to perform the essential functions of a job, please email jobs@avride.ai.

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