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How much do adversarial machine learning jobs pay per hour?

As of May 31, 2026, the average hourly pay for adversarial machine learning in the United States is $21.33, according to ZipRecruiter salary data. Most workers in this role earn between $18.75 and $22.84 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Adversarial Machine Learning specialist, and why are they important?

To excel in Adversarial Machine Learning, you need a strong background in machine learning, deep learning, statistics, and computer science, typically supported by an advanced degree in a related field. Familiarity with frameworks like TensorFlow or PyTorch, experience with adversarial attack and defense libraries, and knowledge of security protocols are crucial. Creative problem-solving, critical thinking, and strong communication skills help in designing robust models and explaining complex threats to stakeholders. These competencies are vital to anticipate vulnerabilities, safeguard AI systems, and ensure the reliability of machine learning models in real-world applications.

What are some common challenges faced by professionals working in Adversarial Machine Learning roles?

Adversarial Machine Learning professionals often face the challenge of staying ahead of rapidly evolving attack techniques that can compromise model integrity and security. Managing the balance between model performance and robustness is another key difficulty, as defenses against adversarial attacks can sometimes reduce accuracy or increase computational costs. Collaboration with data scientists, security teams, and software engineers is vital for developing resilient models and implementing effective defenses. Staying current with the latest research and tools is essential for success in this dynamic field.

What is adversarial machine learning?

Adversarial machine learning is a field of study focused on understanding and defending against attacks that manipulate machine learning models by feeding them deceptive input, known as adversarial examples. These attacks can cause models to make incorrect predictions, raising concerns about the security and reliability of AI systems, especially in critical applications like image recognition and autonomous vehicles. Researchers in this area develop techniques to detect, prevent, and mitigate these vulnerabilities to make machine learning systems more robust.

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

AspectAdversarial Machine LearningData Scientist
CredentialsKnowledge of machine learning, cybersecurity, and threat detectionDegree in data science, statistics, or related fields
Work EnvironmentResearch labs, cybersecurity teams, AI developmentBusiness analytics, data analysis, model development
Industry UsageAI security, cybersecurity, machine learning researchBusiness, finance, healthcare, tech companies

Adversarial Machine Learning focuses on understanding and defending AI models against malicious inputs, often within cybersecurity contexts. Data Scientists analyze data to extract insights, build models, and support decision-making across various industries. While both roles require machine learning knowledge, Adversarial Machine Learning emphasizes security and robustness, whereas Data Scientists focus on data analysis and predictive modeling.

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What job categories do people searching Adversarial Machine Learning jobs look for? The top searched job categories for Adversarial Machine Learning jobs are:
Adversarial Machine Learning jobs near you
Infographic showing various Adversarial Machine Learning job openings in the United States as of May 2026, with employment types broken down into 41% Full Time, 55% Part Time, and 4% Contract. Highlights an 87% Physical, 8% Hybrid, and 5% Remote job distribution, with an average salary of $44,363 per year, or $21.3 per hour.

Adversarial Machine Learning Engineer

C-Serv

Portland, OR • On-site

Full-time

Medical, Life

Posted 10 days ago

Job description

The Opportunity
We are building a dedicated AI Red Team to rigorously test and harden enterprise-scale AI products.
We are looking for an adversarial machine learning specialist who thinks like an attacker.
This role focuses on identifying vulnerabilities in LLM-driven systems, breaking model guardrails, exploiting data pathways, and stress-testing AI deployments before they reach enterprise customers.
This is a hands-on technical role at the core of AI security.
What You'll Do
  • Conduct adversarial testing across LLM and AI-based systems
  • Execute real-world attack simulations, including:
  • Prompt injection
  • Jailbreaking and guardrail bypass
  • Data exfiltration attempts
  • Model inversion and evasion techniques
  • RAG manipulation
  • Develop scripts and tooling to automate attack scenarios
  • Analyse model behaviour under adversarial pressure
  • Identify systemic vulnerabilities in:
  • APIs
  • Embedding pipelines
  • Vector databases
  • Fine-tuned model implementations
  • Collaborate with engineering teams to validate remediation
  • Document findings clearly and concisely

You will help ensure AI systems are resilient before they are deployed at scale.
Requirements
What We're Looking For
Core Technical Skills
  • Strong experience in adversarial ML or AI security research
  • Experience working with LLM-based systems (OpenAI, Anthropic, open-source models, etc.)
  • Deep understanding of:
  • Prompt injection techniques
  • Model jailbreak methodologies
  • AI system exploitation vectors
  • Strong Python skills
  • Experience building custom attack tooling or experimentation frameworks

AI Systems Knowledge
  • Familiarity with:
    • RAG architectures
    • Vector databases
    • Model fine-tuning workflows
    • API-based model deployments
    • Understanding of model safety mechanisms and guardrails

Nice to Have
  • Background in cybersecurity or penetration testing
  • Familiarity with OWASP LLM Top 10
  • Experience working in enterprise environments

Who You Are
  • Curious and relentless
  • Comfortable thinking like an attacker
  • Creative in finding non-obvious vulnerabilities
  • Detail-oriented but fast-moving
  • Comfortable operating in ambiguity
  • Independent but collaborative

You don't just run test cases - you design new ones.
Benefits
  • Comprehensive Private Medical Coverage
  • Support for Mental Health Expenses
  • Life Insurance Options
  • Attractive Compensation Package

Apply