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

Design and deliver Generative AI, LLM, retrieval‑augmented generation (RAG), and agentic AI ... Proficiency in Python and modern software engineering practices, including APIs, testing, CI/CD ...

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How much do generative ai testing jobs pay per hour?

As of Sep 9, 2026, the average hourly pay for generative ai testing in Riverside, NJ is $54.26, according to ZipRecruiter salary data. Most workers in this role earn between $44.66 and $62.16 per hour, depending on experience, location, and employer.

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 Riverside, NJ?

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

What job categories do people searching Generative Ai Testing jobs in Riverside, NJ look for?

The top searched job categories for Generative Ai Testing jobs in Riverside, NJ are:

What cities near Riverside, NJ are hiring for Generative Ai Testing jobs?

Cities near Riverside, NJ with the most Generative Ai Testing job openings:

AI Validation, Senior Specialist

Malvern, PA

Vangard, Inc.
Convention and Trade Show Organizers • 11 - 50 employees

Full-time

Posted 8 days ago


Job description

The Model Risk Management (MRM) Team, part of Vanguard's second line of defense, is seeking an experienced model risk professional to support independent oversight of AI/ML and generative AI models built and/or used across the enterprise.

Vanguard is a global investment management firm with a mission to give investors the best chance for investment success. Across its businesses and support functions, Vanguard uses AI to drive effectiveness, efficiency, and a range of key business outcomes.

In this role, you will lead the independent validation and challenge of AI models, ensuring they are conceptually sound, wellgoverned, and fit for purpose. You will work closely with data scientists, quantitative developers, model users, and technology partners, providing credible challenge while maintaining strong, collaborative relationships with the business.

Beyond individual model reviews, you will help drive the evolution of Vanguard's model risk management framework, shaping methodologies, standards, and practices that support consistent, enterprisewide application of model risk principles.

Core Responsibilities


  • Assess models and systems using generative AI throughout their lifecycle including leading independent validations and assessment of ongoing monitoring, change management, and remediations of model risk findings

  • Provide effective challenge of model assumptions, methodologies, data, testing, implementation, monitoring and limitations, with a focus on material risk drivers

  • Assess compliance with internal AI standards, emerging regulatory requirements, and governance expectations related to AI/ML and generative AI systems, including fairness, explainability, transparency, and human oversight

  • Ensure timely execution of model validation and high-quality validation reports that meet internal standards, and communicate validation findings clearly to technical and nontechnical stakeholders

  • Develop, maintain and enhance validation procedures to support a consistent, risk-based approach for independent validation and effective challenge of AI/ML and generative AI systems, including keeping informed of the evolution of technology in generative and agentic AI

  • Contribute to the design and enhancement of model risk management policies, standards, and procedures across the full model lifecycle (inventory, development, validation, approval, and ongoing monitoring)

  • Support the MRM team in advising stakeholders on Model Risk policy and other relevant standards as needed

  • Identify emerging and top risks across the portfolio of AI/ML and generative AI models and surface them to MRM leadership

  • Provide coaching and mentorship to junior team members

  • Participate in special projects and perform other duties as assigned


Qualifications

  • PhD in a quantitative discipline such as Computer Science, Mathematics, Statistics, Physics, Engineering or an equivalent combination of training and experience

  • Minimum of 7 years of relevant experience in model development or model validation

  • Experience developing, testing or validating AI/ML models or systems using generative AI is required

  • Knowledge of programming languages including Python is a requirement

  • Knowledge of technology platforms used for model development and deployment, CI/CD is a requirement

  • Knowledge of generative AI architectures including foundation models, RAG systems, agentic workflows, prompt engineering approaches, and associated risks such as hallucinations, algorithmic bias, and harmful outputs

  • Knowledge of AI/ML modeling including ML algorithms, NLP, deep learning, and evaluation methodologies

  • Exceptional written communication skills are required, including the ability to communicate complex model risks to audiences ranging from technical practitioners to senior executives

  • Experience in the financial services sector is a plus

  • Broad knowledge of risk management at financial institutions preferred

  • Ability to deal effectively with a variety of stakeholder teams

  • Ability to effectively manage multiple and competing priorities

  • Exhibits flexibility and excellentjudgment

Special Factors

Sponsorship

Vanguard is offering visa sponsorship for this position.

About Vanguard

At Vanguard, we don't just have a mission-we're on a mission.

To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.

How We Work

Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.