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Adversarial Machine Learning Jobs (NOW HIRING)

Cmu

Senior AI Security Software Engineer

Arlington, VA · On-site +1

Additionally, we work in generative AI and large language models, data visualization, security analysis of AI systems, and adversarial machine learning. We have access to a wide variety of cyber ...

Eccalon LLC

Machine Learning Engineer

Detroit, MI · On-site

Generative Adversarial Architectures. Preferred qualifications * MS. or PhD in Machine Learning, or related field * Extensive AWS or GCP experience putting scalable Machine Learning systems into ...

Cmu

AI Security Software Engineer

Pittsburgh, PA · On-site +1

Additionally, we work in generative AI and large language models, data visualization, security analysis of AI systems, and adversarial machine learning. We have access to a wide variety of cyber ...

Cmu

Senior AI Security Software Engineer

Pittsburgh, PA · On-site +1

Additionally, we work in generative AI and large language models, data visualization, security analysis of AI systems, and adversarial machine learning. We have access to a wide variety of cyber ...

Cmu

AI Security Software Engineer

Arlington, VA · On-site +1

Additionally, we work in generative AI and large language models, data visualization, security analysis of AI systems, and adversarial machine learning. We have access to a wide variety of cyber ...

MIT Lincoln Laboratory

AI/ML Programmer

Lexington, MA · On-site

... adversarial machine learning / AI security experience • Advanced AI/ML knowledge - solid graduate-level coursework or equivalent work experience in machine-learning theory, deep learning, NLP ...

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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 cities are hiring for Adversarial Machine Learning jobs? Cities with the most Adversarial Machine Learning job openings:
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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.
Machine Learning Engineer

Machine Learning Engineer

Prospance Inc.

Mountain View, CA

Other

Posted 10 days ago

Job description

Job Title: Machine Learning Engineer 
Only on W2 
Duration: 12 months
Location: Mountain View, CA (Local candidates Required)
 
Position Summary
We are looking for an experienced Machine Learning Engineer to lead the development of prompt injection and prompt safety models that protect Client''''s downstream agentic AI systems across phone, cloud, and XR/AR. You will design, train, and deploy classifier and guardrail models (both cloud-based and hybrid on-device) that screen agent inputs and outputs for injection attacks, unsafe content, and policy violations. A core part of the role is post-training these models with RLHF, DPO, and related optimization techniques to push detection accuracy and false-positive rates beyond what off-the-shelf solutions provide.
 
Role and Responsibilities
  • Design and train prompt injection detection models and prompt safety classifiers that operate on both inputs to and outputs from Samsung''''s agentic AI systems.
  • Build hybrid deployment pipelines that split safety inference between on-device (phone, XR/AR) and cloud, optimizing for latency, privacy, and detection coverage.
  • Apply post-training techniques (e.g. RLHF, reward modeling, policy optimization) to optimize guardrail model performance, calibration, and robustness against adaptive adversaries.
  • Curate and generate adversarial training data: direct and indirect prompt injections, jailbreaks, tool-use exploits, and unsafe-output cases drawn from red-teaming and production signals.
  • Build evaluation harnesses that measure attack success rate, false-positive rate, latency, and on-device footprint across model iterations and threat categories.
  • Partner with agent, device, and platform teams to integrate safety models into mobile-use agents, XR/AR assistants, and cloud agentic workflows, and to close the loop from production incidents back into training data.
  • Work cross-functionally with security researchers, modeling teams, and product engineers; document methods and, where appropriate, contribute to patents and publications.
 
Required Qualifications
  • M.S. or Ph.D. in Computer Science, Machine Learning, Electrical Engineering, or a related field; or B.S. with equivalent industry experience.
  • 3+ years of industry experience in ML engineering or applied AI research, with demonstrated ownership of production ML systems.
  • 2+ years of industry experience in software engineering.
  • Strong proficiency in Python and PyTorch (or JAX/TensorFlow), with solid software engineering fundamentals (version control, testing, and reproducible experimentation).
  • Hands-on experience post-training LLMs with RLHF, DPO, RLAIF, or reward modeling including reward design, preference data curation, and training stability.
  • Hands-on experience training and deploying classifier or guardrail models for safety, content moderation, abuse detection, or adversarial robustness.
  • Familiarity with prompt injection, jailbreak, and agentic AI threat models, and with distributed training frameworks (DeepSpeed, FSDP, Accelerate).
 
Preferred Qualifications:
  • Experience building safety or moderation systems for agentic AI: tool-use guardrails, indirect prompt injection defenses, or output filtering for autonomous agents.
  • Experience with red-teaming, adversarial data generation, or automated attack pipelines (e.g., GCG)

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