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Generative Ai Testing Jobs in California (NOW HIRING)

Writer

Software engineer, generative AI

San Francisco, CA · On-site

$111K - $304K/yr

As a software engineer, generative ai at WRITER, you'll be at the forefront of expanding human ... Early-detection cancer testing through Galleri * Flexible spending account and dependent FSA ...

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Generative Ai Testing information

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 much do AI testers get paid?

AI testers, involved in evaluating and validating generative AI models, typically earn salaries ranging from $60,000 to $120,000 annually depending on experience, location, and company size. Entry-level positions may start lower, while experienced testers with specialized skills in machine learning and data analysis can earn higher wages.

Is AI testing a good career?

AI testing, including roles like Generative AI Testing, is a growing field with increasing demand for skills in machine learning, data analysis, and programming. It offers opportunities in tech companies, research labs, and startups, often requiring knowledge of AI frameworks and testing tools. The career can be stable and rewarding for those with technical expertise and an interest in AI development and quality assurance.

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 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 is a $900,000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers, AI research directors, or executive positions, often requiring advanced skills, extensive experience, and sometimes specialized certifications. These roles usually involve leading AI development projects, strategic planning, and overseeing AI teams in large organizations or tech companies.

How do I become an AI tester?

To become an AI tester, you should have a strong understanding of machine learning concepts, programming skills in languages like Python, and experience with data annotation and model evaluation. Familiarity with AI development tools and testing frameworks, along with attention to detail, is essential for identifying issues in AI systems.

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 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 June 2026, with employment types broken down into 13% Internship, 68% Full Time, 13% Part Time, and 6% Temporary. Highlights an 75% In-person, 6% Hybrid, and 19% Remote job distribution.

Python / Generative AI Engineer

Prophecy Technologies

Los Angeles, CA • On-site

Full-time

Posted 14 days ago

Job description

JOB SUMMARY
The Python / Generative AI Engineer will design, develop, and implement AI-driven applications leveraging Generative AI technologies. The role focuses on building scalable AI solutions using Python, developing Retrieval-Augmented Generation (RAG) systems, and integrating AI models within cloud environments. The engineer will work closely with data scientists, engineers, and DevOps teams to implement GenAI solutions, optimize prompts, and deploy AI applications using modern cloud and container technologies.
Location
Los Angeles, CA / Irvine, CA (Hybrid)
Experience
5+ Years
Key Responsibilities
• Design and develop scalable applications using Python and SQL.
• Implement Generative AI solutions leveraging modern AI frameworks and tools.
• Build and maintain Retrieval-Augmented Generation (RAG) systems for AI-powered applications.
• Develop prompt engineering strategies to improve GenAI model outputs.
• Integrate AI solutions with cloud platforms and enterprise systems.
• Deploy and manage AI workloads using AWS, Docker, and DevOps pipelines.
• Collaborate with cross-functional teams to translate business problems into AI-driven solutions.
• Optimize AI workflows and ensure performance, reliability, and scalability.
• Implement and manage the Generative AI lifecycle including development, testing, deployment, and monitoring.
• Troubleshoot and resolve issues related to AI model integration and deployment.
Required Skills & Experience
• Minimum 5+ years of strong hands-on experience in Python development.
• Strong proficiency in SQL for data processing and analysis.
• Hands-on experience in Generative AI development using Python.
• Experience building Retrieval-Augmented Generation (RAG) based AI systems.
• Strong knowledge of prompt engineering and GenAI model interaction.
• Experience with AWS cloud services.
• Experience with containerization technologies such as Docker.
• Familiarity with DevOps practices and CI/CD pipelines.
• Strong analytical thinking, problem-solving, and critical reasoning skills.
• Ability to work independently with strong ownership and accountability.
Competencies
• Python Development
• Generative AI Development
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• Cloud Computing (AWS)
• DevOps & Containerization
• SQL & Data Processing
Preferred Skills
• Experience using LangChain for building AI agents and GenAI workflows.
• Experience designing enterprise-level AI applications.
• Exposure to AI/ML model lifecycle management and deployment frameworks.

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