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Generative Ai Testing Jobs in California (NOW HIRING)

Inherent Technologies

AI Architect

San Jose, CA · On-site

This role focuses on creating advanced AI applications, including Generative AI systems like ... Ensure code quality and maintainability through unit testing, code reviews, and adherence to best ...

Redolent, Inc.

AI Engineer with Java background

Sunnyvale, CA · On-site

$61.75 - $84.50/hr

Design, develop, and deploy AI-powered applications and solutions using modern LLMs and Generative ... Participate in architecture discussions, code reviews, testing, deployment, and ongoing support ...

SAP

SAP iXp Intern - Full-Stack AI Developer

Palo Alto, CA · On-site +1

$22.75 - $29.75/hr

Develop Generative AI use cases such as intelligent agents, enterprise search, summarization ... Participate in code reviews, debugging, testing, and AI evaluation to ensure high-quality ...

SAP

SAP iXp Intern - Full-Stack AI Developer

Palo Alto, CA · On-site

$22 - $29/hr

Develop Generative AI use cases such as intelligent agents, enterprise search, summarization ... Participate in code reviews, debugging, testing, and AI evaluation to ensure high-quality ...

SAP

$22.75 - $29.75/hr

Develop Generative AI use cases such as intelligent agents, enterprise search, summarization ... Participate in code reviews, debugging, testing, and AI evaluation to ensure high-quality ...

Compunnel

AI Engineer

San Francisco, CA · On-site

The role requires end-to-end ownership of production-grade Generative AI systems, including ... testing, troubleshooting, and implementation of software enhancements across the full stack. • ...

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Generative Ai Testing information

What is generative AI testing?

Generative AI Testing refers to the process of evaluating and validating AI systems, particularly those that generate content such as text, images, or code. This type of testing focuses on assessing the accuracy, reliability, fairness, and safety of generative models to ensure they function as intended and avoid producing harmful or biased outputs. Testers use various methods, including automated and manual techniques, to check for issues like hallucinations, inappropriate content, or security vulnerabilities. The goal is to build trust in generative AI systems and ensure they meet quality and ethical standards before deployment.

What are some common challenges faced when testing generative AI models, and how can I prepare to address them in this role?

Testing generative AI models often involves unique challenges such as evaluating the quality and relevance of generated content, detecting bias or inappropriate outputs, and ensuring model consistency across various prompts. You may work closely with data scientists and engineers to create robust evaluation frameworks and develop automated as well as manual testing strategies. Familiarity with prompt engineering, statistical evaluation techniques, and domain-specific knowledge will help you address these challenges effectively. Proactively staying updated on industry best practices and collaborating with cross-functional teams are key to success in this dynamic field.

What are the key skills and qualifications needed to thrive as a generative AI testing specialist, and why are they important?

To thrive as a Generative AI Testing Specialist, you need a robust understanding of machine learning principles, model evaluation techniques, and a background in computer science or a related field. Familiarity with tools such as Python, TensorFlow, PyTorch, and model evaluation frameworks, as well as experience with automated testing platforms, is typically required. Analytical thinking, attention to detail, and strong communication skills help you identify model weaknesses and collaborate effectively with development teams. These skills are crucial to ensure the reliability, safety, and ethical deployment of generative AI solutions.

What is the difference between Generative Ai Testing vs Data Scientist?

AspectGenerative Ai TestingData Scientist
Required CredentialsKnowledge of AI models, testing tools, programming skillsStatistics, programming, data analysis certifications
Work EnvironmentAI development teams, testing labs, tech companiesResearch labs, tech firms, finance, healthcare
Employer & Industry UsageAI product testing, quality assurance in techData analysis, predictive modeling across industries

Generative Ai Testing focuses on evaluating and validating AI-generated content and models, ensuring quality and accuracy. Data Scientists analyze data, build models, and derive insights. While both roles require programming and AI knowledge, Generative Ai Testing emphasizes testing processes, whereas Data Scientists focus on data analysis and model development.

How do I become a Generative AI Testing?

To become a Generative AI Tester, develop skills in machine learning, natural language processing, and programming languages like Python. Gain experience with AI frameworks such as TensorFlow or PyTorch and understand data quality and model evaluation techniques. Relevant certifications and hands-on projects can enhance your qualifications for roles in AI testing environments.

Is Generative AI Testing a good career?

Generative AI Testing is a growing field within AI development, focusing on evaluating the quality and safety of AI-generated content. It requires skills in machine learning, programming, and understanding AI models, often involving tools like Python and TensorFlow. The role offers opportunities in tech companies and research labs, with demand expected to increase as AI applications expand.

What are popular job titles related to Generative Ai Testing jobs in California?

For Generative Ai Testing jobs in California, the most frequently searched job titles are:

What job categories do people searching Generative Ai Testing jobs in California look for?

The top searched job categories for Generative Ai Testing jobs in California are:

What cities in California are hiring for Generative Ai Testing jobs?

Cities in California with the most Generative Ai Testing job openings:

Generative Ai Testing jobs near you
Infographic showing various Generative Ai Testing job openings in California as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 15% Part Time, 5% Contract, and 1% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

AI Architect

San Jose, CA • On-site

Inherent Technologies
IT Services • 51 - 200 employees
Learn more about Inherent Technologiesexternal

Other

Re-posted 21 days ago

Job description

Job Summary:
This role focuses on creating advanced AI applications, including Generative AI systems like chatbots, data retrieval platforms, and analytics tools. The ideal candidate will bring expertise in a robust understanding of AI/ML technologies, and the ability to seamlessly integrate on-premises infrastructure with Azure/AWS AI cloud services.

Key Responsibilities:

Collaborate with cross-functional teams to conceptualize, design, and implement AI-powered solutions.

Develop and maintain end-to-end software applications using Python and other modern frameworks.

Design and implement scalable APIs and backend services for Generative AI applications such as chatbots and data analytics platforms.

Build responsive and intuitive front-end interfaces using modern JavaScript frameworks (React, Angular, or Vue.js).

Develop and integrate machine learning models into applications, ensuring performance and scalability.

Leverage cloud platforms (Azure, AWS) to deploy AI models, ensuring secure and efficient integration with on-premises systems.

Drive the adoption of DevOps practices, including CI/CD pipelines, containerization (Docker), and orchestration (Kubernetes).

Ensure code quality and maintainability through unit testing, code reviews, and adherence to best practices.

Troubleshoot and resolve complex technical challenges in real-time AI applications.

Stay updated on emerging AI/ML and full-stack development trends to incorporate innovative solutions.

Qualifications:

Education: Bachelor's or master's degree in computer science, Software Engineering, or a related field.

Experience: Minimum 6 8 years of professional experience in full-stack development, with a focus on Python and AI/ML technologies.

Strong expertise in Python frameworks (Django, Flask, FastAPI) and front-end frameworks (React, Angular, Vue.js).

Hands-on experience with machine learning frameworks and libraries, such as TensorFlow, PyTorch, Scikit-learn, or Hugging Face.

Proficiency in developing and deploying AI/ML models in cloud environments like Azure AI, AWS SageMaker, or Google AI Platform.

Deep understanding of integrating on-premises systems with cloud-based AI solutions.

Experience with relational and NoSQL databases (PostgreSQL, MySQL, MongoDB).

Familiarity with containerization (Docker) and orchestration tools (Kubernetes).

Knowledge of CI/CD pipelines and infrastructure as code tools (Jenkins, Terraform, or Ansible).

Strong problem-solving skills and the ability to work in fast-paced, dynamic environments.

Preferred Skills:

Experience with Natural Language Processing (NLP) and Generative AI models like GPT, BERT, or similar.

Knowledge of big data tools like Apache Spark or Hadoop.

Familiarity with microservices architecture.

Certifications in cloud platforms (Azure, AWS) or AI/ML tools.