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Adversarial Machine Learning Jobs in Washington (NOW HIRING)

Red teaming and adversarial testing * Hallucination detection * Bias and fairness assessments ... Machine learning algorithms * Deep learning techniques * Natural language processing (NLP)

Red teaming and adversarial testing * Hallucination detection * Bias and fairness assessments ... Machine learning algorithms * Deep learning techniques * Natural language processing (NLP)

AI Red Teamer, Cyber

Washington, DC ยท Remote

$100K - $120K/yr

Develop adversarial testing methodologies to evaluate system security, robustness, and resilience ... Experience testing AI, machine learning, or large language model applications * Familiarity with ...

AI Red Teamer, Cyber

Washington, DC ยท On-site

$100K - $120K/yr

Our adversarial red teaming, model evaluations, and intelligence collection enable engineering ... Experience testing AI, machine learning, or large language model applications * Familiarity with ...

Our adversarial red teaming, model evaluations, and intelligence collection enable engineering ... Software Engineering, Data Engineering, or Machine Learning. In this role, you will: * Collaborate ...

Artificial Intelligence & Machine Learning * Frontier models (large language models, foundation models), adversarial AI, AI red-teaming, ethics and safety, AI applications in cyber defense and ...

... in machine learning, robotics, autonomous systems, computer vision, or control theory * 3+ years ... or adversarial ML * Proven record of transitioning research into practical applications ...

... in machine learning, robotics, autonomous systems, computer vision, or control theory * 3+ years ... or adversarial ML * Proven record of transitioning research into practical applications ...

AI Data Engineer - Manager

Mclean, VA ยท On-site

$115K - $139K/yr

Lead the development of AI models (e.g., machine learning, natural language processing, computer ... Address potential issues such as training data poisoning, AI model theft, and adversarial samples.

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

Senior Advisor - Artificial Intelligence and Machine Learning

Systems Planning and Analysis

Alexandria, VA โ€ข On-site

$138K - $138K/yr

Full-time

Posted 4 days ago


Job description

Overview
Systems Planning and Analysis, Inc. (SPA) delivers high-impact, technical solutions to complex national security issues. With over 50 years of business expertise and consistent growth, we are known for continuous innovation for our government customers, in both the US and abroad. Our exceptionally talented team is highly collaborative in spirit and practice, producing Results that Matter. Come work with the best! We offer opportunity, unique challenges, and clear-sighted commitment to the mission. Objective. Responsive. Trusted.
The AUKUS, Submarines, and Industrial Base Group (ASIG), within the Sea, Land, Air Division, provides timely, objective, analytic assessments that integrate technical, operational, programmatic, policy and business analysis to Director Submarine Program directorates and key stakeholders surrounding submarine platform construction. Director Submarine Program unifies submarine platform procurement activities, which includes the Columbia-class SSBN program (a Major Defense Acquisition Program and the Navy's top acquisition priority), the Virginia-class SSN program, the new SSN-X program (developing the next generation attack submarine), and the AUKUS Integration and Acquisition Office, to bring improved capabilities to our undersea forces. Analysts and Engineers supporting Director Submarine Program will continue SPA's decades of support to the SSBN force, as well as supporting oversight of cutting-edge attack submarine construction, assisting in developing the design requirements for the SSN platform of the future, and helping plan and execute new and innovative strategies to strengthen the critical submarine shipbuilding industrial base. These are high-profile, major acquisition and manufacturing programs of significant importance to the future capabilities of the US Navy in an era of renewed strategic competition. #MC
#MC
Responsibilities
Serves as the Artificial Intelligence and Machine Learning (AI/ML) Senior Advisor to the Direct Reporting Program Manager Submarines (DRPM SUBS) providing critical support for interagency and intergovernmental decision-making. The primary objective is to provide expert advice to Senior Leaders at all levels-including but not limited to the Secretary of the Navy (SECNAV), Under Secretary of the Navy (UNSECNAV), Office of the Secretary of War (OSW), United States Navy (USN), Assistant Secretary of the Navy for Research, Development, and Acquisition (ASN RD&A), and Chief of Naval Operations (CNO), as well as their staff(s)-to accelerate the adoption and integration of AI/ML capabilities across the submarine enterprise. Additional responsibilities include:
  • Serve as the principal advisor to advise Senior Leaders at all levels including but not limited to the Secretary of the Navy, Under Secretary of the Navy, Chief of Naval Operations (CNO), the Secretary of War, Senior Leaders relevant staff members, and DRPM SUBS leadership on all AI/ML matters, translating complex technical concepts into strategic implications and actionable policy.
  • Serve as the DRPM SUBS foremost technical authority on the current and future state of AI/ML, including deep learning, generative AI, reinforcement learning, and AI assurance.
  • Provide expert guidance to major acquisition programs (PEOs) on the integration of AI/ML into weapon systems, autonomous platforms, and command-and-control systems.
  • Provide expert counsel on the global AI/ML landscape, including adversarial capabilities and intentions, to inform DoW defensive and offensive postures.
  • Develop new paradigms for the Test, Evaluation, Verification, and Validation (TEV&V) of AI-enabled systems to ensure they are robust, reliable, and secure.
  • Assess and advise on the AI-readiness of the defense industrial base and provide strategies for fostering innovation and partnership with non-traditional technology companies.

Qualifications
Required Experience:
  • Nationally or internationally recognized authority in AI/ML with a minimum of 15 years of senior-level experience leading cutting-edge research and/or large-scale application development.
  • Demonstrated success providing strategic counsel on AI/ML to C-suite, Flag Officer, Senior Executive Service (SES), or equivalent leadership in a large, complex organization (e.g., FAANG-level tech company, national lab, or major research university).
  • A portfolio of significant accomplishments, such as pioneering widely adopted AI/ML models, publishing seminal research papers, holding key patents, or leading a major AI/ML product or division.
  • Deep, technical, state-of-the-art knowledge across multiple AI/ML domains.

Desired Experience:
  • Experience as a Chief Technology Officer (CTO), Chief Scientist, or senior research fellow at a leading technology firm or research institution.
  • Experience applying AI/ML to defense, intelligence, or national security problems (e.g., ISR data analysis, autonomous navigation, cybersecurity, logistics).
  • Familiarity with the DoD acquisition process and the unique challenges of deploying software-intensive systems in a military environment.
  • Ph.D. in Computer Science, Artificial Intelligence, Data Science, or a related field.

Clearance:
  • Must be able to maintain a Top Secret/SCI security clearance.