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Generative Ai Testing Jobs in Massachusetts (NOW HIRING)

This role is ideal for someone who can move fluidly from ambiguity to action - working with stakeholders to define problems, rapidly testing ideas using modern generative AI tools, and helping the ...

... generative AI patterns (e.g., retrieval-augmented generation, tool calling, and workflow ... Quality & operations: testing, logging/metrics, and basic monitoring for services in development ...

Stay up-to-date with the latest advancements in Generative AI, prompt engineering, Agentic ... build processes, testing, and operations. * Strong collaboration and elaboration skills ...

Artificial Intelligence (AI) Engineer

Boston, MA · On-site

$105K - $145K/yr

... testing, support, and stakeholder management. Also responsible for supporting the creation of best ... generative AI, machine learning, and deep learning techniques and identifies opportunities to ...

Artificial Intelligence (AI) Engineer

Boston, MA · On-site

$105K - $144K/yr

... testing, support, and stakeholder management. Also responsible for supporting the creation of best ... generative AI, machine learning, and deep learning techniques and identifies opportunities to ...

Senior Data Scientist

Boston, MA · On-site

$107.45 - $199.55/hr

Demonstrate proficiency in developing, fine‑tuning, and deploying Generative AI models such as ... build processes, testing, and operations. * Strong collaboration and elaboration skills ...

... generative AI, agentic AI, and decision-science problem statements. * Perform exploratory data analysis, statistical analysis, hypothesis testing, experimental design, feature engineering, predictive ...

... cross-validation, robustness testing, fairness audits, and post-deployment monitoring ... Strong understanding of Generative AI ethics and governance frameworks: The candidate should ...

Lead AI Engineer

Boston, MA · Hybrid

$111K - $146K/yr

Why Now, Why Mirakl Generative AI and AI Agents are fundamentally reshaping commerce software. At ... Promote best practices for evaluation, testing, guardrails, and observability * Review designs and ...

Showing results 41-60

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 Massachusetts?

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

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

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

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

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

Infographic showing various Generative Ai Testing job openings in Massachusetts as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 9% Part Time, 3% Contract, and 1% Nights. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution.

Associate Director, AI & Application Security - HYBRID ROLE

Vrtx

Boston, MA • Hybrid

$63.75 - $85.25/hr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 12 days ago


Job description

Job Description

This is a hybrid position that requires 3 days a week in our Boston office

Vertex is seeking an Associate Director, AI & Application Security, to lead security for AI-enabled applications, platforms, and services across the enterprise. This role is responsible for securing AI throughout the full lifecycle-from design and development to deployment and ongoing operations-including generative AI, agentic workflows, traditional machine learning, and AI embedded in enterprise applications.

This leader will help define how Vertex securely adopts and scales AI across Azure, AWS, and GCP, as well as third-party and foundation model platforms such as Microsoft Copilot / Azure OpenAI, Anthropic, Google Gemini, and AWS Bedrock. The role will partner closely with technical and business stakeholders to establish pragmatic guardrails, strengthen secure development practices, and reduce risk without slowing innovation.

The ideal candidate brings deep expertise in cloud security and application security, along with strong judgment, technical credibility, and the ability to influence decisions in fast-moving, evolving environments. This role also requires practical experience applying security and risk frameworks relevant to AI and modern application environments.

Key Duties and Responsibilities

  • Lead AI and application security across the full lifecycle of AI-enabled systems, from design and development through deployment and operations.
  • Define and evolve security standards, guardrails, and control expectations for AI systems used across Vertex.
  • Apply and operationalize industry-recognized security frameworks and control models, including:
    • NIST AI Risk Management Framework (AI RMF)
    • NIST Cybersecurity Framework (CSF)
    • OWASP Top 10
    • OWASP Top 10 for LLM and Generative AI Applications
  • Secure AI workloads and AI-enabled applications across cloud and SaaS environments, with emphasis on:
    • policy enforcement
    • data protection
    • logging and telemetry
    • monitoring and operational visibility
  • Lead threat modeling and misuse-case analysis for AI systems, including risks such as:
    • prompt injection and prompt abuse
    • sensitive data leakage
    • tool or action abuse
    • unsafe outputs
    • model misuse
  • Define and mature AI guardrails, including monitoring, detection, logging, and misuse or negative testing practices.
  • Establish secure development expectations for AI-enabled applications and services, including secure coding practices and appropriate separation of development and production environments.
  • Build and lead application security testing practices for AI-enabled applications and supporting services, including SAST, DAST, automated scanning, and retesting processes.
  • Partner with Cloud Security, Security Operations, Privacy, Legal, Data Science, and Engineering teams to align security controls with business, technical, and regulatory requirements.
  • Influence architecture and platform decisions through practical, risk-based guidance that can scale with AI adoption.
  • Communicate risks, tradeoffs, and recommendations clearly to both technical teams and senior leadership.

Knowledge and Skills

  • Cloud security architecture and controls across Azure and AWS
  • Familiarity with GCP security concepts and services
  • Secure software development lifecycle (SDLC) practices
  • Secure coding standards and code review practices
  • SAST, DAST, automated security scanning, and remediation workflows
  • OWASP Top 10 and common application and API security risks
  • Familiarity with OWASP guidance for LLM/GenAI applications
  • API security, identity and access management, secrets management, and service-to-service trust
  • Logging, telemetry, monitoring, and detection for cloud-native environments
  • Threat modeling and misuse-case analysis
  • Familiarity with AI security risks, including:
    • prompt injection
    • data leakage
    • model misuse
    • tool or action abuse
    • unsafe outputs
    • policy enforcement
  • Familiarity with AI platforms and providers such as:
    • Microsoft Copilot / Azure OpenAI
    • Anthropic
    • Google Gemini
    • AWS Bedrock
    • emerging AI platforms and services

Education and Experience

  • Bachelor's degree in Computer Science, Information Security, Engineering, or a related field or equivalent experience.
  • Significant experience in application security, product security, cloud security, or a related cybersecurity discipline.
  • Strong experience securing cloud environments, particularly Azure and AWS; familiarity with GCP is a plus.
  • Deep knowledge of application security fundamentals and secure software development practices.
  • Experience securing APIs, platforms, and complex distributed systems.
  • Experience leading threat modeling, architecture reviews, and risk-based security assessments.
  • Experience applying security and risk frameworks in engineering environments, including familiarity with NIST AI RMF, NIST CSF, and common application security standards.
  • Demonstrated ability to partner effectively with engineering and platform teams to embed security into design and delivery processes.
  • Experience securing generative AI applications, agentic workflows, or machine learning-enabled services.
  • Experience defining AI guardrails and monitoring strategies at scale.
  • Excellent communication and influence skills, with the ability to engage both technical teams and senior leaders.

Preferred Qualifications

  • Experience working in biopharmaceutical or other GxP-regulated environments with strong privacy and data protection requirements.

#LI-HYBRID

Pay Range:

$172,000 - $258,000

Disclosure Statement:

The range provided is based on what we believe is a reasonable estimate for the base salary pay range for this job at the time of posting. This role is eligible for an annual bonus and annual equity awards. Some roles may also be eligible for overtime pay, in accordance with federal and state requirements. Actual base salary pay will be based on a number of factors, including skills, competencies, experience, and other job-related factors permitted by law.

At Vertex, our Total Rewards offerings also include inclusive market-leading benefits to meet our employees wherever they are in their career, financial, family and wellbeing journey while providing flexibility and resources to support their growth and aspirations. From medical, dental and vision benefits to generous paid time off (including a week-long company shutdown in the Summer and the Winter), educational assistance programs including student loan repayment, a generous commuting subsidy, matching charitable donations, 401(k) and so much more.

Flex Designation:

Remote-Eligible

Flex Eligibility Status:

In this Remote-Eligible role, you can choose to be designated as:
1. Remote: work remotely five days per week and come into the office on occasion - you're always welcome on-site; or select
2. Hybrid: work remotely up to two days per week; or select
3. On-Site: work five days per week on-site with ad hoc flexibility.

Note: The Flex status for this position is subject to Vertex's Policy on Flex @ Vertex Program and may be changed at any time.

#LI-Remote

Company Information

Vertex is a global biotechnology company that invests in scientific innovation.

Vertex is committed to equal employment opportunity and non-discrimination for all employees and qualified applicants without regard to a person's race, color, sex, gender identity or expression, age, religion, national origin, ancestry, ethnicity, disability, veteran status, genetic information, sexual orientation, marital status, or any characteristic protected under applicable law. Vertex is an E-Verify Employer in the United States. Vertex will make reasonable accommodations for qualified individuals with known disabilities, in accordance with applicable law.

Any applicant requiring an accommodation in connection with the hiring process and/or to perform the essential functions of the position for which the applicant has applied should make a request to the recruiter or hiring manager, or contact Talent Acquisition at ApplicationAssistance@vrtx.com