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Adversarial Machine Learning Jobs in Texas (NOW HIRING)

Scan Ninja Inc.

AI/ML Engineer - Cybersecurity

Houston, TX · On-site

$100 - $130/hr

Experience with adversarial machine learning or secure AI systems * Background in vulnerability management or cybersecurity operations * Hands-on experience with vector databases and retrieval ...

New

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 ...

HiddenLayer

Lead Engineer, AI Attack Simulation

Austin, TX · On-site +1

$101K - $133K/yr

Experience with AI red teaming, adversarial machine learning, AI security evaluation, or autonomous security testing. * Direct experience with breach and attack simulation, adversary emulation ...

Posted today

GM Financial

Cybersecurity AI_ML Engineer

Arlington, TX · On-site

... machine learning methodologies to transform cybersecurity data into scalable detection capabilities, enhance analytics, and improve threat detection under complex and adversarial conditions. In this ...

Mastech Digital

Senior Security Engineer

Irving, TX · On-site

$109K - $150K/yr

Familiarity with adversarial ML concepts, such as prompt injection, model inversion, and model ... AI/ML certifications (e.g., Microsoft Azure AI Engineer, AWS ML Specialty, GIAC Machine Learning ...

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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 August 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 100% In-person job distribution.

AI/ML Engineer - Cybersecurity

Scan Ninja Inc.

Houston, TX • On-site

$100 - $130/hr

Other

Posted 2 days ago

New

Job description

You will work on applied AI systems that drive real security outcomes, not just benchmark scores. This role focuses on model quality, reliability, observability, and measurable impact for enterprise customers.

Responsibilities
  • Fine-tune, evaluate, and improve LLM workflows for vulnerability triage
  • Build data pipelines for training, evaluation, and feedback collection
  • Work closely with security engineers to validate model outputs in real-world scenarios
  • Define and monitor model quality, reliability, and production metrics
  • Ship AI features safely using staged rollouts and appropriate controls
Requirements
  • Experience building and shipping machine learning or AI systems
  • Strong knowledge of NLP, LLMs, and modern model workflows
  • Working knowledge of cybersecurity concepts and use cases
Must Have
  • Production experience with machine learning systems
  • Strong Python skills and a solid foundation in model evaluation
  • Experience with prompt design, retrieval workflows, and LLM application patterns
  • Ability to make practical tradeoffs between quality, latency, and cost
Nice to Have
  • Experience with adversarial machine learning or secure AI systems
  • Background in vulnerability management or cybersecurity operations
  • Hands-on experience with vector databases and retrieval systems
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