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Adversarial Machine Learning Jobs in Texas (NOW HIRING)

HiddenLayer

AI Red Teamer

Austin, TX · On-site +1

The ideal candidate is a proactive problem solver with hands-on experience in AI security testing and a deep understanding of machine learning models and adversarial techniques. WHO WE ARE:

IBS Software

Senior Machine Learning Scientist

Austin, TX · On-site

$90K - $123K/yr

... starter to join as a Senior Machine Learning Scientist for our Consulting and Digital ... Prompt injection and adversarial inputs * Hallucination in long-horizon reasoning * Unsafe or ...

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Adversarial Machine Learning information

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 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 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.

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 are popular job titles related to Adversarial Machine Learning jobs in Texas? For Adversarial Machine Learning jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Adversarial Machine Learning jobs in Texas look for? The top searched job categories for Adversarial Machine Learning jobs in Texas are:
What cities in Texas are hiring for Adversarial Machine Learning jobs? Cities in Texas with the most Adversarial Machine Learning job openings:
Adversarial Machine Learning jobs near you
Infographic showing various Adversarial Machine Learning job openings in Texas as of July 2026, with employment types broken down into 79% Full Time, and 21% Contract. Highlights an 80% In-person, and 20% Remote job distribution.
Trust & Safety Engineer (GenAI) - Remote

Trust & Safety Engineer (GenAI) - Remote

micro1 AI

Houston, TX • Remote

$50 - $90/hr

Part-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Role Title: AI Jailbreak & Prompt-Injection Security Expert


Role Type: Contractor


Location: Remote


micro1 is engaging AI Jailbreak & Prompt-Injection Security Experts to contribute to a cutting-edge customer initiative focused on AI safety and robustness. In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input. No prior experience in AI is required — your domain knowledge is what matters.


Scope of Work

  1. Design and implement advanced methodologies for evaluating AI system safety, focusing on ethical jailbreaks, LLM red teaming, prompt injection, and tool-use abuse scenarios.
  2. Create comprehensive cross-domain elicitation strategies to uncover multi-turn and complex adversarial bypass patterns in AI models.
  3. Develop, maintain, and update regression test suites that systematically test for jailbreak susceptibility and prompt-injection vulnerabilities.
  4. Construct robust evaluation frameworks that stress-test AI models against real-world adversarial threats, aiming to enhance overall system robustness.
  5. Collaborate with technical stakeholders to translate security findings into actionable improvements for model safety and risk mitigation.
  6. Document methodologies, findings, and best practices in clear, well-structured written reports and presentations for both technical and non-technical audiences.


Preferred Qualifications

  1. 2+ years of expertise in adversarial machine learning, LLM red teaming, AI safety evaluation, or a closely related security domain
  2. Proven experience researching, testing, or uncovering vulnerabilities related to ethical jailbreaks, prompt injection, tool-use abuse, or adversarial AI attacks.
  3. Advanced degree (PhD, MS) in computer science, cybersecurity, machine learning, or a relevant discipline, or equivalent operational/professional background.
  4. High credibility and recognition within the AI security or adversarial ML community—such as published research, open-source tools, or conference presentations.
  5. Exceptional written and verbal communication skills, with a strong focus on clear documentation and collaborative problem-solving.
  6. Prior participation in multi-disciplinary projects or cross-functional AI safety initiatives is a plus.
  7. Familiarity with current LLM architectures, prompt engineering techniques, and security assessment tools is highly desirable.

Apply