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Adversarial Machine Learning Robust Jobs (NOW HIRING)

... and developing robust evaluation frameworks for advanced AI systems. Requirements Key ... Required Qualifications * 2+ years of experience in AI Safety, Adversarial Machine Learning, LLM ...

The postdoctoral researcher will conduct cutting-edge research in areas such as cyber-physical systems security, protection of critical infrastructure, and adversarial machine learning. The position ...

Sr. Machine Learning Engineer

Santa Clara, CA · On-site

$143K - $189K/yr

... adversarial evasion, jailbreaking, and multimodal content filtering ... If you are passionate about protecting users and building robust ML systems at scale, we want you ...

Senior Machine Learning Engineer I // II

Denver, CO · On-site +1

$107K - $147K/yr

... and robust statistical practices. Our Culture We value tenacity, curiosity, and a hunger for ... adversarial machine learning forward. The Role As a Senior Machine Learning Engineer , you will be ...

... machine learning models. • Develop testing frameworks for AI robustness, adversarial risks, and model safety. • Evaluate Large Language Models (LLMs) and Generative AI applications for ...

If you are passionate about protecting users and building robust ML systems at scale, we want you ... Experience in red-teaming, adversarial training, or robustness evaluation of ML models. Building ...

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 ...

If you are passionate about protecting users and building robust ML systems at scale, we want you ... Experience in red-teaming, adversarial training, or robustness evaluation of ML models. Building ...

... robust production real-time and batch decisioning solutions • Ensure operational and business ... adversarial trends, identify behavior patterns, and respond with agile logic changes • ...

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Adversarial Machine Learning Robust information

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$32K

$80.6K

$132K

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.

Senior Machine Learning Research Scientist - SAIL

Pittsburgh, PA • On-site

Software Engineering Institute | Carnegie Mellon University

$95K - $121K/yr

Full-time

Re-posted 22 days ago


Job description

Job Summary:
Carnegie Mellon University is a leading research institution focused on advancing artificial intelligence and machine learning. They are seeking a Senior Machine Learning Research Scientist to conduct research on AI vulnerabilities and develop strategies to secure AI systems against adversaries.
Responsibilities:
• build real-world, mission-scale AI capabilities through solving practical engineering problems
• discover and define the processes, practices, and tools to support operationalizing AI for robust, secure, scalable, and human-centered mission capabilities
• prepare our customers to be ready for the unique challenges of adopting, deploying, using, and maintaining AI capabilities
• identify and investigate emerging AI and AI-adjacent technologies that are rapidly transforming the technology landscape
• conduct and lead novel research in applied machine learning and artificial intelligence
• work with and lead interdisciplinary teams to turn research results into prototype operational capabilities for government customers and stakeholders
• work with the leadership team and colleagues to plan, develop, and carry out an overall research strategy, and to influence the national research agenda regarding future technology
• actively participate on teams of software developers, researchers, designers, and technical leads
• build relationships and collaborate with researchers, government customers, and other stakeholders to understand challenges, needs, possible solutions, and research directions
• contribute to improving the overall technical capabilities of the Division by mentoring and teaching others, participating in design (software and otherwise) sessions, and sharing insights and wisdom across the SEI AI Division
Qualifications:
Required:
• A bachelor’s degree in computer science, statistics, machine learning, electrical engineering, or related discipline with ten (10) years of experience; OR MS in the same fields with eight (8) years of experience; OR PhD in with five (5) years of experience
• Willingness to work onsite at an SEI facility 5 days per week.
• Be able to obtain and maintain an active Department of War security clearance.
• Willing to travel up to 25% of the time to locations outside of your home location. Travel sites include SEI offices in Pittsburgh and Washington, D.C., sponsor sites, and conferences.
• Comprehensive knowledge of machine learning; previous experience in adversarial machine learning preferred but not required
• A track record of conducting research and applying scientific methods to solve difficult problems
• Experience leading research projects in novel areas with limited previous work to build upon
• Ability to work with leadership to plan, develop, and deliver an overall research strategy
• Strong written and verbal communication skills; ability to convey complex technical ideas in a layperson’s terms
• Proficiency in writing funding proposals or pitching ideas for new research projects
• Ample experience with publishing written or technical artifacts showcasing your work
• Strong collaboration skills for working with colleagues and sponsors
• Willingness to guide and mentor junior team members
Preferred:
• Previous experience in adversarial machine learning
Company:
We conduct cutting-edge research and development that accelerates the transition of technology to the Department of War (DoW), delivering measurable impact in support of the national security mission. Founded in 1984, the company is headquartered in Pittsburgh, USA, with a team of 501-1000 employees. The company is currently Late Stage.