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How much do adversarial machine learning robust jobs pay per year?

As of Sep 9, 2026, the average yearly pay for adversarial machine learning robust in the United States is $80,627.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,000.00 and $108,000.00 per year, depending on experience, location, and employer.

What is adversarial machine learning robustness?

Adversarial machine learning robustness refers to the ability of a machine learning model to maintain its performance and resist manipulation when exposed to adversarial examples—inputs that have been intentionally modified to deceive the model. In this field, researchers and engineers develop techniques to detect, defend against, and reduce the impact of such attacks on AI systems. Robust models are crucial in applications like security, autonomous vehicles, and healthcare, where adversarial attacks could have serious consequences. Achieving robustness involves strategies like adversarial training, input preprocessing, and model architecture improvements.

What are the key skills and qualifications needed to thrive as an adversarial machine learning robustness engineer?

To thrive as an Adversarial Machine Learning Robustness Engineer, you need expertise in machine learning, deep learning, statistics, and a strong background in computer science or a related field. Familiarity with frameworks like TensorFlow and PyTorch, experience with adversarial attack/defense libraries (e.g., CleverHans, Foolbox), and, in some cases, relevant certifications in AI or security are typical requirements. Creative problem-solving, analytical thinking, and effective communication are valuable soft skills for navigating complex vulnerabilities and collaborating across teams. These skills are essential for developing resilient AI systems that can withstand adversarial threats and ensure the reliability of deployed models.

What are some common challenges faced by professionals working in adversarial machine learning robustness, and how are they typically addressed?

Professionals in Adversarial Machine Learning Robustness often face challenges such as designing models that can withstand sophisticated adversarial attacks, keeping up with rapidly evolving attack techniques, and balancing robustness without sacrificing model performance. Addressing these issues usually involves implementing adversarial training, regularly evaluating models against new types of attacks, and collaborating with cross-functional teams (such as security and software engineering) to deploy and monitor robust systems. Continuous learning and staying updated with the latest research are also crucial for success in this dynamic field.

What is the difference between Adversarial Machine Learning Robust vs Data Scientist?

AspectAdversarial Machine Learning RobustData Scientist
Required CredentialsAdvanced knowledge in machine learning, cybersecurity, and statisticsDegree in data science, statistics, or related field
Work EnvironmentResearch labs, cybersecurity teams, AI development firmsBusiness analytics, product teams, consulting firms
Industry UsageAI security, cybersecurity, machine learning researchBusiness intelligence, marketing, finance, tech
Search & Comparison IntentUnderstanding robustness in AI models against adversarial attacksAnalyzing data to inform business decisions

Adversarial Machine Learning Robust specialists focus on developing AI models resilient to malicious attacks, often working in cybersecurity and AI research. Data Scientists analyze data to extract insights for business strategies. While both roles require strong analytical skills, Adversarial Machine Learning Robust professionals emphasize security and robustness, whereas Data Scientists focus on data analysis and visualization.

What other helpful pages are available for Adversarial Machine Learning Robust?

Other pages related to Adversarial Machine Learning Robust:

Infographic showing various Adversarial Machine Learning Robust job openings in the United States as of September 2026, with employment types broken down into 50% Full Time, and 50% Contract. Highlights an 100% In-person job distribution, with an average salary of $80,627 per year, or $38.8 per hour.

Cyber Digital Trust & Online Safety Manager

Hartford, CT • On-site

Deloitte
Finance and Insurance • 10K+ employees
Learn more about Deloitteexternal

Full-time

Re-posted 20 days ago

Key responsibilities

  • Advise clients on developing, managing, and implementing policies, procedures, and strategies to ensure online safety and compliance.

  • Monitor and assess the effectiveness of content moderation systems, identify vulnerabilities, and recommend improvements.

  • Collaborate with cross-functional stakeholders to strengthen security, trust, safety, and responsible use of generative AI models.

Deloitte rating

8.2

Company rating: 8.2 out of 10

Based on 93 frontline employees who took The Breakroom Quiz

48th of 154 rated financial services

Job description

Cyber Digital Trust and Online Safety Manager

The Digital Trust & Online Protection Professional will advise clients in developing, managing, and implementing policies, procedures, and strategies to ensure a safe, compliant, and trustworthy environment for our users. This individual will scale and mature digital trust and safety processes, including content compliance, user protection, and regulatory adherence across our platforms for our clients. Working closely with cross-functional stakeholders, this role will monitor regulatory changes, manage risks, and enhance our organization's approach to content safety, user trust, and online integrity.

Recruiting for this role ends on 12/31/3026.

Work you'll do

As a Manager, Strategy, Growth, and Transformation on the Deloitte Cyber team, you will be responsible for:

  • Designing and executing testing scenarios to identify how prompts or user inputs could be manipulated to generate harmful, misleading, or misaligned generative artificial intelligence outputs.
  • Researching emerging prompt injection, jailbreak, and adversarial testing techniques to evaluate model weaknesses, bias, factual inaccuracy, and misalignment with user intent.
  • Assessing the effectiveness of content moderation systems in detecting unsafe outputs and documenting vulnerabilities, failure patterns, and potential misuse impacts.
  • Recommending improvements to moderation policies, flagging mechanisms, training data, and governance controls based on testing findings.
  • Collaborating with generative artificial intelligence development, content moderation, and cross-functional stakeholders to strengthen security, trust, safety, and responsible use outcomes.
  • Developing multimodal test content and novel prompt manipulation methods to identify failure modes across text and other model inputs.

A successful candidate would possess these skills:

  • Ability to work independently and collaborate as part of a team
  • Effective written and verbal communication skills
  • Meticulous attention to detail and quality of work product
  • Ability to build and sustain professional relationships
  • Ability to lead projects or workstreams
  • Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
  • Strong interpersonal skills and professional demeanor
  • Ability to meet deadlines
  • Ability to mentor and provide clear guidance to others

The team

Enables trust and safety of online communications and digital products, protecting users, consumers, and patients from harm. Enables clients to provide consumer confidence in knowing with whom they are dealing and ensuring the integrity of access to data.

Qualifications

Required:

  • Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Cybersecurity, Linguistics, Psychology, or a related field, or equivalent professional experience
  • 10+ years of experience in threat modeling and simulation, prompt generation and analysis, novel testing, and reporting and improvement
  • Demonstrated hands-on experience, portfolio work, publications, or research in prompt injection, jailbreak testing, model evaluation, adversarial machine learning, multimodal artificial intelligence safety, or generative artificial intelligence vulnerability assessment
  • Ability to travel 25-50%, on average, based on the work you do and the clients and industries/sectors you serve.
  • Limited immigration sponsorship may be available.

Preferred:

  • Doctor of Philosophy (PhD) in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Cybersecurity, Linguistics, Psychology, or a related field, or equivalent professional experience
  • Specialized training or certifications in generative artificial intelligence red teaming, adversarial machine learning, artificial intelligence security, cybersecurity, responsible artificial intelligence, or artificial intelligence governance
  • Experience designing and operationalizing trust and safety testing programs for large-scale consumer platforms, including escalation workflows, issue triage, and remediation tracking
  • Experience working with product, legal, policy, and engineering stakeholders to translate risk findings into practical platform controls and governance improvements

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $134,500 to $265,100.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

#CyberDTP27

Qualifications:

Cyber Digital Trust and Online Safety Manager

The Digital Trust & Online Protection Professional will advise clients in developing, managing, and implementing policies, procedures, and strategies to ensure a safe, compliant, and trustworthy environment for our users. This individual will scale and mature digital trust and safety processes, including content compliance, user protection, and regulatory adherence across our platforms for our clients. Working closely with cross-functional stakeholders, this role will monitor regulatory changes, manage risks, and enhance our organization's approach to content safety, user trust, and online integrity.

Recruiting for this role ends on 12/31/3026.

Work you'll do

As a Manager, Strategy, Growth, and Transformation on the Deloitte Cyber team, you will be responsible for:

  • Designing and executing testing scenarios to identify how prompts or user inputs could be manipulated to generate harmful, misleading, or misaligned generative artificial intelligence outputs.
  • Researching emerging prompt injection, jailbreak, and adversarial testing techniques to evaluate model weaknesses, bias, factual inaccuracy, and misalignment with user intent.
  • Assessing the effectiveness of content moderation systems in detecting unsafe outputs and documenting vulnerabilities, failure patterns, and potential misuse impacts.
  • Recommending improvements to moderation policies, flagging mechanisms, training data, and governance controls based on testing findings.
  • Collaborating with generative artificial intelligence development, content moderation, and cross-functional stakeholders to strengthen security, trust, safety, and responsible use outcomes.
  • Developing multimodal test content and novel prompt manipulation methods to identify failure modes across text and other model inputs.

A successful candidate would possess these skills:

  • Ability to work independently and collaborate as part of a team
  • Effective written and verbal communication skills
  • Meticulous attention to detail and quality of work product
  • Ability to build and sustain professional relationships
  • Ability to lead projects or workstreams
  • Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
  • Strong interpersonal skills and professional demeanor
  • Ability to meet deadlines
  • Ability to mentor and provide clear guidance to others

The team

Enables trust and safety of online communications and digital products, protecting users, consumers, and patients from harm. Enables clients to provide consumer confidence in knowing with whom they are dealing and ensuring the integrity of access to data.

Qualifications

Required:

  • Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Cybersecurity, Linguistics, Psychology, or a related field, or equivalent professional experience
  • 10+ years of experience in threat modeling and simulation, prompt generation and analysis, novel testing, and reporting and improvement
  • Demonstrated hands-on experience, portfolio work, publications, or research in prompt injection, jailbreak testing, model evaluation, adversarial machine learning, multimodal artificial intelligence safety, or generative artificial intelligence vulnerability assessment
  • Ability to travel 25-50%, on average, based on the work you do and the clients and industries/sectors you serve.
  • Limited immigration sponsorship may be available.

Preferred:

  • Doctor of Philosophy (PhD) in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Cybersecurity, Linguistics, Psychology, or a related field, or equivalent professional experience
  • Specialized training or certifications in generative artificial intelligence red teaming, adversarial machine learning, artificial intelligence security, cybersecurity, responsible artificial intelligence, or artificial intelligence governance
  • Experience designing and operationalizing trust and safety testing programs for large-scale consumer platforms, including escalation workflows, issue triage, and remediation tracking
  • Experience working with product, legal, policy, and engineering stakeholders to translate risk findings into practical platform controls and governance improvements

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $134,500 to $265,100.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

#CyberDTP27

Education:Bachelor's DegreeEmployment Type:

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