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Generative Ai Testing Jobs in California (NOW HIRING)

Icwgroup

Applied AI Engineer

San Diego, CA

Implement end-to-end generative AI solutions, including model fine-tuning, deployment, and testing in production environments. * Collaborate with cloud engineering, AI, and ML Ops teams to ...

ICW Group

Applied AI Engineer

San Diego, CA · On-site

Implement end-to-end generative AI solutions, including model fine-tuning, deployment, and testing in production environments. * Collaborate with cloud engineering, AI, and ML Ops teams to ...

Icwgroup

Applied AI Engineer

San Diego, CA · On-site

Implement end-to-end generative AI solutions, including model fine-tuning, deployment, and testing in production environments. * Collaborate with cloud engineering, AI, and ML Ops teams to ...

Hippocratic AI

Senior AI Engineer

Palo Alto, CA · On-site

$180 - $260/hr

Design, build, and optimize production‑grade AI pipelines that power our voice‑based generative ... Drive continuous improvement in model evaluation, safety testing, and observability, ensuring every ...

Hippocratic AI

Staff AI Engineer

Menlo Park, CA · On-site

About Us Hippocratic AI is the leading generative AI company in healthcare. We have the only system ... Own the safety and evaluation bar across model evaluation, safety testing, and observability.

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 ...

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 ...

Shield AI

Principal Engineer, Software (R5285)

San Diego, CA · On-site

$143K - $192K/yr

... I testing • Experience with Electron (or similar). • Building debugging/inspection UIs for generative AI systems • Experience with user experience design video games • Experience designing ...

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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 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. Certifications in AI or data science can enhance your qualifications and improve job prospects.

Is Generative AI Testing a good career?

Generative AI Testing is a growing field within AI development that involves evaluating the quality and safety of AI-generated content. It requires skills in machine learning, programming, and understanding AI models, making it a promising career path with increasing demand as AI technologies expand. Professionals in this area can find opportunities in tech companies, research labs, and startups focused on AI innovation.

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 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:
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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, 75% Full Time, 19% Part Time, 2% Temporary, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution.

Lead Generative AI Analyst

Welocalize, Inc.

San Diego, CA • On-site

Full-time

Posted 17 days ago

Welocalize rating

6.5

Company rating: 6.5 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

330th of 485 rated business services

Job description

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Job Responsibilities:

The Lead Generative AI Analyst plays a pivotal role in leading technical and writing teams to deliver NEMO products. This position also involves customer-facing responsibilities, ensuring world-class quality through tracking metrics, labeling initiatives, and training stakeholders. Exposure to potentially sensitive content may be required.

Responsibilities:

  • Lead technical and writing teams to deliver NEMO products.
  • Act as the liaison between customers, DataFactory teams, product teams, and research teams.
  • Track annotator metrics to ensure top-quality deliverables.
  • Write creative prompts and responses across diverse topics.
  • Manage labeling initiatives with third-party firms and internal customers.
  • Develop and update detailed guidelines and specifications for stakeholders.
  • Train teams on best practices for creating large language models and datasets.
  • Handle potentially toxic or offensive content as required (e.g., sexually explicit, graphic, or disturbing material).

Additional Job Details:

Requirements:

  • Proven track record of delivering quality products on time.
  • Native-level English proficiency with excellent written and verbal communication skills.
  • Self-driven, motivated, and enthusiastic about working on state-of-the-art machine learning tools.
  • Domain knowledge in specialized fields (e.g., Law, Gardening, Biology, US History, Academic Engineering).
  • 4-year accredited college degree or equivalent experience.

Ways to Stand Out:

  • Over 1 year of experience delivering products in the LLM space.
  • College degree or experience in Linguistics, English Literature, Creative Writing, Journalism, STEM, or relevant domain knowledge.
  • Deep understanding of large language models and RLHF.
  • Experience in labeling and tagging frames/tasks/prompts for DNN preparation.
  • QA/testing experience.
  • Python scripting skills are a plus.

What Welocalize employees say

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Hours and flexibility

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