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Autonomous Driving Jobs in Texas (NOW HIRING)

Torc Robotics

Autonomous Fleet Specialist

Fort Worth, TX · On-site

$62K - $74K/yr

A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing ...

PlusAI

Software Integration Engineer

San Antonio, TX

$125K - $175K/yr

Your responsibilities will include creating and executing end-to-end software test plans for autonomous driving, supporting development on either the planning or MCU teams, and generating tooling and ...

PlusAI

Software Integration Engineer

San Antonio, TX · On-site +1

$125K - $175K/yr

Your responsibilities will include creating and executing end-to-end software test plans for autonomous driving, supporting development on either the planning or MCU teams, and generating tooling and ...

Kett Engineering

Autonomous CDL A Driver

Elmendorf, TX · On-site

$27 - $30/hr

Autonomous CDL A Driver San Antonio, TX - Elmendorf, TX 78112 Overview Salary Range $27.00 - $30.00 ... Conducts test driving per customer test specifications. Performs total vehicle testing as well as ...

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Autonomous Driving information

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How much do autonomous driving jobs pay per hour?

As of Aug 10, 2026, the average hourly pay for autonomous driving in Texas is $19.91, according to ZipRecruiter salary data. Most workers in this role earn between $16.11 and $20.14 per hour, depending on experience, location, and employer.

What are some common challenges faced by professionals working in autonomous driving roles?

Professionals in autonomous driving often encounter challenges such as integrating complex sensor data, ensuring the reliability of AI algorithms in unpredictable environments, and meeting strict safety and regulatory standards. Collaboration is key, as teams typically work cross-functionally with hardware, software, and quality assurance groups to solve technical issues and validate system performance. Adapting to rapidly evolving technology and maintaining clear communication across disciplines are essential for success in this dynamic field.

What is the difference between Autonomous Driving vs Autonomous Vehicle Software Engineer?

AspectAutonomous DrivingAutonomous Vehicle Software Engineer
Required CredentialsEngineering degrees, specialized training in AI, robotics, or automotive systemsComputer science, software engineering degrees, programming skills in C++, Python
Work EnvironmentResearch labs, automotive companies, testing facilitiesSoftware development teams, automotive tech companies, simulation environments
Industry UsageDeveloping and testing autonomous vehicle systemsDesigning and coding software for autonomous vehicle functions
Common Search/ComparisonYesYes

Autonomous Driving focuses on the overall development, testing, and deployment of self-driving vehicle systems, often involving hardware integration and real-world testing. Autonomous Vehicle Software Engineers primarily concentrate on coding, software design, and algorithm development for autonomous vehicle functionalities. Both roles require technical expertise, but Autonomous Driving is broader, encompassing system integration, while Autonomous Vehicle Software Engineers specialize in software development within the autonomous vehicle industry.

What are the key skills and qualifications needed to thrive in an autonomous driving engineer role, and why are they important?

To thrive as an Autonomous Driving Engineer, you need a solid background in computer science, robotics, and engineering, often demonstrated by a relevant degree and experience in machine learning or computer vision. Proficiency with programming languages such as Python and C++, as well as familiarity with simulation tools, ROS (Robot Operating System), and sensor integration (e.g., LiDAR, radar), is essential. Strong problem-solving abilities, collaboration skills, and adaptability help you effectively tackle complex technical challenges and work within multidisciplinary teams. These skills ensure the development of safe, reliable, and innovative autonomous vehicle systems that meet rigorous industry standards.

What is autonomous driving?

Autonomous driving refers to the use of technology to enable vehicles to navigate and operate without direct human input. These vehicles use a combination of sensors, cameras, radar, and artificial intelligence to perceive their environment and make driving decisions. The goal is to improve road safety, reduce human error, and increase transportation efficiency. Autonomous driving systems are typically categorized by levels, ranging from driver assistance to fully self-driving vehicles. This field is rapidly evolving, with ongoing research and real-world testing by automotive and technology companies.

Which car companies offer autonomous driving?

Several major car manufacturers offer autonomous driving features, including Tesla, General Motors (with its Super Cruise system), Ford, BMW, Mercedes-Benz, and Volvo. These companies develop and deploy varying levels of driver-assistance technologies, often requiring specialized skills in software, sensors, and safety standards for autonomous vehicle development.
What are popular job titles related to Autonomous Driving jobs in Texas? For Autonomous Driving jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Autonomous Driving jobs in Texas look for? The top searched job categories for Autonomous Driving jobs in Texas are:
What cities in Texas are hiring for Autonomous Driving jobs? Cities in Texas with the most Autonomous Driving job openings:
Autonomous Driving jobs near you
Infographic showing various Autonomous Driving job openings in Texas as of August 2026, with employment types broken down into 74% Full Time, 24% Part Time, and 2% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution, with an average salary of $41,421 per year, or $19.9 per hour.

Senior Algorithm Engineer, Deep Learning & Vision

Bot Auto

Houston, TX • On-site

$99K - $137K/yr

Full-time

Posted 13 days ago

Job description

Company Introduction

At Bot Auto, we are revolutionizing the transportation of goods with our cutting-edge autonomous trucks, enhancing the quality of life for communities around the globe. With the agility of a start-up and the wisdom of seasoned experts, Bot Auto boasts a team that has achieved numerous world-firsts and unparalleled innovations. United by a shared vision, we create miracles and propel the future of transportation. Join us and transform your dreams into reality.

Key Responsibilities
  • Explore and propose new ideas using your knowledge and experience in deep learning, neural networks and large foundation models in autonomous driving including: end-to-end object detection, tracking and prediction, end-to-end planning and control, and end-to-end autonomous driving system, end-to-end online mapping. SLAM in localization and etc..
  • Work on the entire life cycle of machine learning projects from data analysis, model experimentations to performance metrics verifications, and understand the entire workflow in great detail.
  • Be exposed to many cross-team projects and collaborate with the product, simulation and other sibling autonomous driving algorithm teams to extend machine learning technology to all components.
QualificationsRequired: 
  • Have an advanced degree (Ph.D or Master's) in related fields of study: computer science, computer engineering, robotics, mathematics, physics, and etc.
  • Have in-depth knowledge and extensive experience in machine learning, and/or computer vision, modern transformer architecture, and employ SOTA techniques of machine learning.
  • Be familiar with PyTorch, TensorFlow and other machine learning platforms and tools.
  • Have strong motivation to work independently in a fast paced environment while collaborating with other teams on more complex and larger projects.
Preferred: 
  • Have a proven track record of research publications in top conferences and/or journals as the first author.
  • Have knowledge and experience of generative models, model distillation, or model inference acceleration (e.g. TensorRT) techniques.

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