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Generative Ai Testing Jobs in Riverside, CA (NOW HIRING)

Essc

AI Software Developer

Irvine, CA · On-site

Responsible for designing and implementing generative AI solutions using Copilot Studio and Azure ... Must pass all drug testing required by ESSC. Carrying/Lifting: Occasional / 0-30 lbs Standing:

Niagara Bottling

AI/ML Engineer II

Diamond Bar, CA

This role requires hands-on experience applying machine learning, Generative AI, and intelligent ... control, testing, and migration practices. * Plan, track, escalate, and deliver assigned tasks ...

Capgroup

Senior Machine Learning Engineer

Irvine, CA · On-site

$112K - $154K/yr

You will join ahigh-performingteam of ML engineers and software engineers building Generative AI ... Maintain a high bar for code quality, testing, clarity, and reliability. * Contribute to ...

Rivian

Staff Security Engineer - AI Security

Irvine, CA

$140K - $175K/yr

Automate adversarial testing: Build AI-driven frameworks to scale our Red Teaming and vulnerability ... Deep understanding of generative AI systems, including RAG pipelines, large language models, vector ...

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Generative Ai Testing information

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How much do generative ai testing jobs pay per hour?

As of Jun 12, 2026, the average hourly pay for generative ai testing in Riverside, CA is $56.05, according to ZipRecruiter salary data. Most workers in this role earn between $46.15 and $64.18 per hour, depending on experience, location, and employer.

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 Riverside, CA? For Generative Ai Testing jobs in Riverside, CA, the most frequently searched job titles are:
What job categories do people searching Generative Ai Testing jobs in Riverside, CA look for? The top searched job categories for Generative Ai Testing jobs in Riverside, CA are:
What cities near Riverside, CA are hiring for Generative Ai Testing jobs? Cities near Riverside, CA with the most Generative Ai Testing job openings:
Generative Ai Testing jobs near you

Senior Machine Learning Engineer (Generative AI)

Purple Drive Technologies

Irvine, CA • On-site

$112K - $154K/yr

Full-time

Posted 21 days ago

Job description

Overview:
Remote (Must work PST hours)
Job Overview:
We are seeking a highly experienced Senior ML Engineer with deep expertise in Generative AI and Machine Learning to drive innovation and deliver large-scale production-ready ML solutions. The ideal candidate will bring hands-on experience across the full SDLC, strong technical leadership, and proven success in deploying cutting-edge ML models and platforms at scale.
Key Responsibilities:
  • Act as a thought leader driving ML innovation across products and platforms.
  • Lead the full software development lifecycle (SDLC): design, coding, testing, deployment, and operations.
  • Guide technical strategy, conduct design/code reviews, and mentor peers.
  • Develop production-grade ML code for next-gen real-time ML platforms.
  • Extend and optimize existing ML libraries/frameworks for performance at scale.
  • Partner with scientists and engineers to accelerate model development, validation, experimentation, and integration into production systems.
Required Qualifications:
  • 8+ years of experience across the full SDLC (design, coding, testing, deployment, operations).
  • 5+ years of hands-on experience building and deploying end-to-end ML solutions in production.
  • Proven experience with Generative AI (RAG, AI Agents, LLM fine-tuning).
  • Strong background in cloud-based distributed systems (AWS, Azure, GCP).
  • Exceptional problem-solving skills and ability to work in complex, ambiguous environments.
  • Bachelor's degree in Computer Science, Mathematics, or related field.
Preferred Qualifications:
  • MS/PhD in Computer Science, Machine Learning, or related discipline.
  • Experience with Graph ML and Graph technologies (e.g., GNNs, GraphRAG).
  • Exposure to Big Data and distributed technologies (Spark, Flink, Kafka, PySpark, Lakehouse, Druid, Hudi, Glue).

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