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Machine Learning Testing Jobs in Texas (NOW HIRING)

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Machine Learning Testing information

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How much do machine learning testing jobs pay per hour?

As of Jul 10, 2026, the average hourly pay for machine learning testing in Texas is $21.26, according to ZipRecruiter salary data. Most workers in this role earn between $18.37 and $23.75 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Machine Learning Testing position, and why are they important?

To excel in Machine Learning Testing, you need a solid understanding of machine learning concepts, data analysis, and programming skills in languages like Python, as well as a background in quality assurance or software testing. Familiarity with frameworks such as TensorFlow, PyTorch, automated testing tools, and relevant certifications like ISTQB are highly beneficial. Strong attention to detail, analytical thinking, and effective communication skills help testers identify issues and collaborate with data scientists and developers. These competencies are essential to ensure the reliability, fairness, and accuracy of machine learning models deployed in production environments.

What are the typical challenges faced by professionals in Machine Learning Testing roles?

Professionals in Machine Learning Testing often encounter challenges such as dealing with non-deterministic model outputs, insufficient or imbalanced datasets, and unclear or evolving testing criteria. They may need to work closely with data scientists and engineers to develop robust test cases and validation methods tailored for dynamic machine learning systems. Staying updated on advancements in testing methodologies and tools is also important, as the field evolves rapidly. Successfully overcoming these challenges leads to higher quality models and more reliable AI solutions for end users.

What is a Machine Learning Testing job?

A Machine Learning Testing job involves evaluating and validating machine learning models to ensure they function correctly, efficiently, and ethically. This includes testing for accuracy, reliability, bias, and performance under different conditions. Professionals in this role employ techniques such as unit testing, integration testing, data validation, and model performance monitoring. They also work closely with data scientists and engineers to debug issues and improve model robustness. The goal is to ensure that machine learning systems perform as expected and meet business or regulatory requirements.

What are the most commonly searched types of Machine Learning Testing jobs in Texas? The most popular types of Machine Learning Testing jobs in Texas are:
Infographic showing various Machine Learning Testing job openings in Texas as of July 2026, with employment types broken down into 1% As Needed, 76% Full Time, 20% Part Time, 1% Temporary, 1% Contract, and 1% Nights. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $44,224 per year, or $21.3 per hour.

Computer Vision & Machine Learning Engineer

Allen Control Systems

Austin, TX โ€ข On-site

$110K - $130K/yr

Full-time

Medical, Dental, Vision, PTO

Re-posted 10 days ago


Job description

Computer Vision & Machine Learning Engineer for Autonomous Anti-Drone Systems
Company Overview:
Allen Control Systems (ACS) is a cutting-edge defense startup founded by two former Navy electrical engineers with a proven track record in robotics and software. We are developing an autonomous gun turret using advanced computer vision and control systems to precisely detect, track, and neutralize enemy drones.
With an engineering-first culture, ACS values technical excellence and innovation. Backed by our founders' successful exits from two previous venture acquired for a combined $180M in 2022, we are committed to ensuring that the groundbreaking technologies we develop will have a real-world impact.
What You'll Do:
  • Development and optimization of computer vision algorithms for our autonomous gun turret, focusing on real-time drone detection, tracking, and classification.
  • Design and implement machine learning models that can operate in resource-constrained environments while maintaining high accuracy and reliability.
  • Collaborate closely with electrical engineers to integrate computer vision systems into the turret's hardware architecture.
  • Conduct extensive testing and validation of computer vision algorithms in various scenarios to ensure robustness and performance under different environmental conditions.
  • Contribute to the hardening of the prototype turret into a military-grade system, and assist in developing variants for different weapon systems and engagement ranges.

What You'll Need:
  • Deep passion for machine learning, computer vision, and robotics, and have been exploring these areas since early in your career.
  • At least a Bachelor's degree in Computer Science, Electrical Engineering, or a related field, with a strong focus on machine learning and computer vision.
  • 4-7+ years of experience working on machine-learning-based computer vision, ideally in the context of robotics.
  • A proven track record of developing and deploying computer vision systems, ideally in real-time or safety-critical applications.
  • Proficient in Python, C++, and have experience with machine learning frameworks such as TensorFlow, PyTorch, or similar.
  • Experience with embedded systems and integrating computer vision algorithms into hardware.
  • Familiar with various sensors (e.g., cameras, LIDAR, RADAR) and their integration into autonomous systems.
  • You enjoy collaborating with other engineers to solve complex technical challenges.

What We Offer:
  • Competitive salary
  • ACS Equity Package
  • Health, Dental, Vision Insurance
  • Paid Time Off

Allen Control Systems is an Equal Opportunity Employer, providing equal employment opportunities to all employees and applicants for employment. Allen Control Systems prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.