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Adversarial Machine Learning Robust Jobs (NOW HIRING)

HiddenLayer

AI Solutions Architect- Federal

$64.50 - $85/hr

Maintain deep currency in adversarial machine learning and the evolving AI threat landscape, and raise the technical bar of everyone around you. WHO YOU ARE: * 5+ years in solutions architecture ...

HiddenLayer

AI Solutions Architect- Federal

Washington, DC · Remote

$71.25 - $94/hr

Maintain deep currency in adversarial machine learning and the evolving AI threat landscape, and raise the technical bar of everyone around you. WHO YOU ARE: * 5+ years in solutions architecture ...

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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.

AI Security Software Engineer with Security Clearance

Pittsburgh, PA • On-site

Software Engineering Institute
1 - 5K employees
Learn more about Software Engineering Instituteexternal

Other

Retirement

Re-posted 23 days ago

Job description

The CERT Division of the Software Engineering Institute (SEI) is seeking applicants for the role of AI Security Software Engineer. Established in response to the Morris worm, CERT has been a leader in cybersecurity research, advancing the resilience of software systems and responding to sophisticated cyber threats. As AI becomes central to critical infrastructure, advancing its security and resilience offers a compelling opportunity to shape the future, impacting support of the national security mission. Our team researches and develops tactics, techniques, and procedures necessary for the field of AI security. Our work includes experimental designs for large-scale AI security research programs, AI red teaming, and counter AI applications. 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-related data, including malware samples, NetFlow data, cyber security training runs and tests, incident tickets, and more. Artificial Intelligence Security Software Engineers at the SEI use software and machine learning engineering practices, data processing, and data analytics to help our researchers solve AI security challenges. In this role, you will work with our researchers and customers to develop machine learning based prototypes, products, and tools to solve and automate solutions to AI Security problems. You'll get a chance to work with elite AI and cyber security professionals and university faculty to build new technologies that will influence national AI and cyber security strategy for decades to come. Key Responsibilities * Develop machine learning-based prototypes, tools, and systems for AI security applications, demonstrating strong expertise in ML development and deployment
* Collaborate with researchers and stakeholders to design and execute experimental AI security solutions, communicating effectively across technical and non-technical audiences
* Apply software engineering best practices to build scalable, maintainable systems, grounded design principles
* Process and analyze large, diverse cybersecurity datasets (e.g., malware, NetFlow, incident data), using strong analytical and problem-solving skills
* Support AI red teaming and adversarial machine learning initiatives, applying an innovative and research-driven mindset
* Translate research concepts into practical, operational capabilities, with the ability to work independently and as part of a collaborative team Minimum Qualifications * BS in computer science, machine learning, cybersecurity, statistics, or related discipline with eight (8) years of experience; OR MS in the same fields with five (5) years of experience; OR PhD in the same fields with two (2) years of experience
* Understanding of software engineering principles and system design
* Experience with containerization and microservices architectures
* Travel to various locations to support the SEI's overall mission. This includes within the SEI and CMU community, sponsor sites, conferences, and offsite meetings on occasion (5%). * You will be subject to a background check and will need to obtain and maintain a Department of War (DoW) security clearance. Preferred Qualifications * Experience applying statistical modeling and advanced data analytics techniques
* Background in developing AI/ML solutions in real-world settings
* Familiarity with applied machine learning domains (e.g., natural language processing, computer vision, autonomy, audio analysis)
* Experience and knowledge in cybersecurity best practices
* Demonstrated ability to quickly learn and adapt to new technologies and domains
Why Join Us * Contribute to a world-class organization with significant impact on software and national security
* Work with cutting-edge technologies alongside leading experts in AI, cybersecurity, and software engineering
* 8% employer retirement contribution (no employee contribution required)
* Tuition benefits for employees and their dependent children
* Flexible work arrangements and strong work-life balance
* Paid parental and military leave
* Professional development opportunities, including conference attendance and certifications
* Qualify for relocation assistance and so much more. Location
Arlington, VA, Pittsburgh, PA
Job Function
Software/Applications Development/Engineering
Position Type
Staff - Regular
Full time/Part time
Full time
Pay Basis
Salary
More Information: * Please visit "Why Carnegie Mellon" to learn more about becoming part of an institution inspiring innovations that change the world.
* Click here to view a listing of employee benefits
* Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran .
* Statement of Assurance

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