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Adversarial Machine Learning Jobs in Washington (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 ...

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 ...

10a Labs

Machine Learning Engineer

Washington, DC · On-site

$130K - $200K/yr

Our adversarial red teaming, model evaluations, and intelligence collection enable engineering ... We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build ...

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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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 Washington? For Adversarial Machine Learning jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Adversarial Machine Learning jobs in Washington look for? The top searched job categories for Adversarial Machine Learning jobs in Washington are:
Adversarial Machine Learning jobs near you
Infographic showing various Adversarial Machine Learning job openings in Washington as of August 2026, with employment types broken down into 100% Full Time. Highlights an 75% In-person, 8% Hybrid, and 17% Remote job distribution.

AI Security Software Engineer

Software Engineering Institute | Carnegie Mellon University

Arlington, VA • On-site

Full-time

Re-posted 18 days ago

Job description

Job Summary:
Carnegie Mellon University's Software Engineering Institute is seeking an AI Security Software Engineer to advance cybersecurity research and the resilience of software systems. The role involves developing machine learning-based prototypes and collaborating with researchers to address AI security challenges.
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
Qualifications:
Required:
• 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
• 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
Preferred:
• 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
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.

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