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Remote Artificial Intelligence Testing Jobs (NOW HIRING)

L3HHCM20

Intern , Artificial Intelligence

Melbourne, FL · On-site +1

$22.50 - $41/hr

Intern,Artificial Intelligence Job Code: 43406 Job Location:Melbourne, FL or Rochester, NY Job ... Support proper regression testing, validation, and documentation of ML models * Identify relevant ...

L3HHCM20

Intern , Artificial Intelligence

Rochester, NY · On-site +1

$22.50 - $41/hr

Intern,Artificial Intelligence Job Code: 43406 Job Location:Melbourne, FL or Rochester, NY Job ... Support proper regression testing, validation, and documentation of ML models * Identify relevant ...

LMI

Artificial Intelligence Lead

Overview LMI is seeking an experienced Artificial Intelligence (AI) Lead to support the General ... This position is anticipated to be majority remote, but with the ability to travel and visit the ...

LMI

Artificial Intelligence Lead

Overview LMI is seeking an experienced Artificial Intelligence (AI) Lead to support the General ... This position is anticipated to be majority remote, but with the ability to travel and visit the ...

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How much do remote artificial intelligence testing jobs pay per hour?

As of Sep 7, 2026, the average hourly pay for remote artificial intelligence testing in the United States is $46.47, according to ZipRecruiter salary data. Most workers in this role earn between $42.07 and $50.96 per hour, depending on experience, location, and employer.

What is the difference between Remote Artificial Intelligence Testing vs Remote Data Scientist?

AspectRemote Artificial Intelligence TestingRemote Data Scientist
Required CredentialsKnowledge of AI models, programming skills, testing frameworksDegree in Data Science, Statistics, or related field; programming skills
Work EnvironmentRemote, collaborative teams, AI development companiesRemote, research-focused or industry companies, data analysis teams
Employer & Industry UsageTech firms, AI startups, software companiesTech companies, finance, healthcare, research institutions
Search & Comparison IntentUnderstanding AI testing roles, job requirementsData analysis, modeling, AI applications

Remote Artificial Intelligence Testing focuses on evaluating AI models and algorithms, ensuring their accuracy and reliability. Remote Data Scientists analyze data, build models, and derive insights. While both roles require programming skills and familiarity with data and AI, AI Testing emphasizes validation and quality assurance of AI systems, whereas Data Scientists focus on data analysis and model development.

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Remote Artificial Intelligence Testing jobs near you
Infographic showing various Remote Artificial Intelligence Testing job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 10% Part Time, 3% Contract, and 1% Nights. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $96,666 per year, or $46.5 per hour.

Artificial Intelligence Software Engineer

10xTalents

Manhattan, NY • On-site, Remote

Full-time

This job post has expired 3 days ago. Applications are no longer accepted.

Job description

Artificial Intelligence Software Engineer About the job Artificial Intelligence Software Engineer Artificial Intelligence Software Engineer - Hybrid/Remote - NYC No visa sponsorship available at this time. Our client an up and coming innovative AI start-up is seeking a highly skilled and innovative Artificial Intelligence Software Engineer to join their dynamic team. In this role, you will be responsible for designing, developing, and implementing AI algorithms and software solutions to solve complex problems across various domains. The ideal candidate will have a strong background in machine learning, deep learning, and software development, with a passion for pushing the boundaries of AI technology.

Responsibilities:

Collaborate with cross-functional teams to understand project requirements and develop AI-driven solutions tailored to specific applications. Design and implement machine learning algorithms and models for tasks such as classification, regression, clustering, and natural language processing.

Develop and optimize neural networks and deep learning architectures for tasks such as image recognition, speech recognition, and recommendation systems. Collect, preprocess, and analyze large datasets to train and evaluate AI models, ensuring robust performance and generalization. Implement scalable and efficient software solutions for deploying AI models in production environments, including cloud-based platforms and edge devices.

Collaborate with software engineers to integrate AI capabilities into existing software systems and develop AI-driven features and products. Research and evaluate emerging technologies and techniques in machine learning and AI, staying abreast of advancements in algorithms, frameworks, and tools. Document design specifications, implementation details, and best practices for internal and external stakeholders. Provide technical guidance and mentorship to junior engineers and contribute to the overall technical expertise of the team.

Requirements:

Bachelor's degree in Computer Science, Electrical Engineering, or related field. Master's or Ph.D. preferred. Proven experience in machine learning, deep learning, and AI software development, with a minimum of 3 years in a relevant role. Proficiency in programming languages such as Python, Java, or C++, and experience with AI frameworks such as TensorFlow, PyTorch, or scikit-learn. Strong understanding of machine learning algorithms, including supervised learning, unsupervised learning, and reinforcement learning.

Experience with deep learning techniques and architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models. Familiarity with software engineering best practices, including version control, testing, and code review. Excellent problem-solving skills and the ability to analyze complex technical challenges and propose innovative solutions.

Strong communication and collaboration skills, with the ability to work effectively in a multidisciplinary team environment. Experience with cloud computing platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes) is a plus.

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