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Machine Learning Testing Jobs in Texas (NOW HIRING)

The role involves developing and optimizing machine learning models, managing large-scale datasets ... Develop efficient workflows for training, validation, and testing, incorporating distributed ...

Utilize A/B testing, statistical, and machine learning models to build robust mechanism for product & feature performance analysis, for evaluation of new product & SW releases and 3rd party product ...

SUMMARY The Machine Learning Engineer provides hands-on expertise in designing, implementing, and ... testing and validation EXPERIENCE AND KNOWLEDGE Bachelor's degree in related field and 5-8 years ...

SUMMARY The Machine Learning Engineer provides hands-on expertise in designing, implementing, and ... testing and validation EXPERIENCE AND KNOWLEDGE Bachelor's degree in related field and 5-8 years ...

Machine Learning Engineer

Austin, TX · On-site

$100 - $130/hr

Machine Learning Engineer page is loaded## Machine Learning Engineerlocations: Austin, TXtime type ... testing and validation**EXPERIENCE AND KNOWLEDGE** • Bachelor's degree in related field and 5-8 ...

SUMMARY The Machine Learning Engineer provides hands-on expertise in designing, implementing, and ... testing and validation EXPERIENCE AND KNOWLEDGE • Bachelor's degree in related field and 5-8 ...

... testing, and integration testing. * Keep track of emerging tech and trends, research the ... grade machine learning systems, spanning model training, tuning, deployment, serving, and ...

Write clean, well-tested, production-quality code and contribute to engineering best practices across testing, reliability and performance. * Research emerging machine learning techniques, prototype ...

Develop and implement comprehensive testing protocols, including smoke tests and unit tests, to ensure solution reliability. * Support the debugging process and issue triage to maintain solution ...

Machine Learning Engineer

Plano, TX · On-site

$62K - $100K/yr

Develop and implement comprehensive testing protocols, including smoke tests and unit tests, to ensure solution reliability. * Support the debugging process and issue triage to maintain solution ...

Machine Learning Engineer

Austin, TX · On-site

$62K - $100K/yr

Develop and implement comprehensive testing protocols, including smoke tests and unit tests, to ensure solution reliability. * Support the debugging process and issue triage to maintain solution ...

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Machine Learning Testing information

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How much do machine learning testing jobs pay per hour?

As of Aug 21, 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 is a machine learning testing?

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 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 are the key skills and qualifications needed to thrive in machine learning testing, 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.

How do I become a machine learning testing?

To become a machine learning testing professional, you typically need a strong background in computer science, programming skills in languages like Python or Java, and knowledge of machine learning frameworks such as TensorFlow or PyTorch. Gaining experience with data analysis, model evaluation, and testing methodologies, along with relevant certifications or training, can improve your qualifications for this role.

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 August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 20% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $44,224 per year, or $21.3 per hour.

Machine Learning Engineer

Avride

Austin, TX • On-site

Full-time

Re-posted 28 days ago


Job description

Job Summary:
Avride develops autonomous vehicle and delivery robot technology, and they are seeking an experienced Machine Learning Engineer to enhance their autonomous systems. The role involves developing and optimizing machine learning models, managing large-scale datasets, and collaborating with cross-functional teams to integrate solutions into real-world applications.
Responsibilities:
• Develop and Optimize Machine Learning Models: Design, implement, and refine deep learning models to ensure efficiency, scalability, and robustness. This may include developing models for understanding a self-driving vehicle’s surroundings or predicting the intentions of other road users.
• Curate and Manage Large-Scale Datasets: Oversee data collection, preprocessing, and augmentation to maintain high-quality datasets for training and evaluation.
• Enhance and Maintain Training Pipelines: Develop efficient workflows for training, validation, and testing, incorporating distributed training, hyperparameter tuning, and automated monitoring.
• Improve Model Deployment and Efficiency: Optimize inference performance, model compression, and deployment across various hardware platforms.
• Explore and Apply Cutting-Edge ML Techniques: Stay up to date with advancements in deep learning and experiment with novel approaches to improve model performance.
• Collaborate with Cross-Functional Teams: Work closely with researchers, software engineers, and robotics experts to integrate machine learning solutions into real-world autonomous systems.
Qualifications:
Required:
• Strong understanding of fundamental machine learning algorithms and neural network techniques.
• Expertise in at least one modern machine learning domain, such as computer vision, large language models, or generative AI.
• At least three years of experience developing neural network-based algorithms, including data collection, training, and deployment.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX, along with PySpark, NumPy, and SciPy.
• Working knowledge of C++ and SQL.
• Ability to quickly absorb new concepts by reviewing research papers, technical reports, and documentation.
• Strong collaboration and communication skills, with the ability to align technical work with business objectives and drive results.
Preferred:
• Advanced degree in Computer Science, Machine Learning, Robotics, or a related field.
• Experience developing ML algorithms for autonomous vehicles or robotics applications.
• Familiarity with neural network deployment and optimization tools such as triton, TensorRT, or similar frameworks.
• Proven ability to set and achieve mid- and long-term goals, prioritize tasks, and meet deadlines independently.
• Experience working in cross-functional teams within a multidisciplinary environment.
• Publications in top-tier ML conferences or contributions to patent applications or ML-related open-source projects.
Company:
Avride is a developer and operator of autonomous vehicles and delivery robots. Founded in 2017, the company is headquartered in Austin, USA, with a team of 201-500 employees. The company is currently Growth Stage.