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Adversarial Machine Learning Jobs in Washington (NOW HIRING)

IntelliGenesis

Senior AI Engineer

Reston, VA · On-site

Experience with adversarial machine learning and AI security * Background in cyber operations or network traffic analysis * Experience deploying models in edge or disconnected environments

New

IntelliGenesis LLC

Senior AI Engineer

Annapolis, MD · On-site

$175K - $220K/yr

Experience with adversarial machine learning and AI security * Background in cyber operations or network traffic analysis * Experience deploying models in edge or disconnected environments

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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.

Senior AI Engineer with Security Clearance

IntelliGenesis

Reston, VA • On-site

$215K - $245K/yr

Other

Medical, Life, Retirement, PTO

Posted 2 days ago

New

Job description

IntelliGenesis is seeking a Senior AI Engineer to lead the design, development, and deployment of production-grade AI systems supporting mission-critical intelligence operations. This role focuses on transitioning AI/ML capabilities from concept to operational environments, including classified and resource-constrained settings.  A day in the life includes architecting scalable AI pipelines, deploying models to edge and cloud environments, integrating AI into cybersecurity workflows, and mentoring junior engineers. You will work closely with cyber operators, software engineers, and infrastructure teams to deliver impactful, real-world AI capabilities—not just research prototypes.  The team dynamic is highly collaborative and mission-driven, consisting of AI engineers, cyber SMEs, and platform engineers working in agile sprints. This role serves as a technical leader and mentor, guiding best practices in MLOps, DevSecOps, and secure AI deployment.  * Lead end-to-end AI system design, development, and deployment 
* Architect and implement scalable MLOps pipelines for training, validation, and deployment 
* Deploy AI/ML models to cloud, on-prem, and edge environments 
* Integrate AI capabilities into cybersecurity tools and operational workflows 
* Ensure system security, including adversarial robustness and secure model deployment 
* Collaborate with cross-functional teams (cyber, infrastructure, software engineering) 
* Mentor mid-level engineers and provide technical oversight 
* Rapidly prototype AI solutions and transition them into production systems 
* Ensure compliance with DoD Risk Management Framework (RMF) requirements Required Qualifications:
* Must be a U.S. Citizen
* Active TS/SCI Clearance and Polygraph required 
* 8+ years of experience in AI/ML engineering, software engineering, or related field 
* Bachelor’s degree in Computer Science, Engineering, or related field
* Strong experience deploying AI/ML models into production environments
* Expertise in Python and at least one additional language (e.g., C++, Go) 
* Experience with MLOps tools (e.g., Kubernetes, Docker, MLflow, Kubeflow, VMWare, Tanzu) 
* Experience with cloud and hybrid infrastructure (AWS, Azure, or DoD cloud environments) 
* Knowledge of DevSecOps and infrastructure-as-code (e.g., Terraform, Ansible) 
* Experience with model serving, monitoring, and lifecycle management 
* Familiarity with cybersecurity concepts and secure system design 
* Experience working in classified or regulated environments (DoD/IC preferred) Desired Qualifications:
* Master's degree
* Experience with adversarial machine learning and AI security 
* Background in cyber operations or network traffic analysis 
* Experience deploying models in edge or disconnected environments 
* Familiarity with large language models (LLMs) and generative AI systems 
* Knowledge of secure enclaves and confidential computing 
* Prior experience supporting DoD or Intelligence Community missions 
* Relevant certifications (e.g., AWS Certified Solutions Architect, Security+, CISSP) 
Compensation Range: $215,000 - $245,000 _____________________________________________________________________________________________________ Compensation ranges encompass a total compensation package and are a general guideline only and not intended as a guaranteed and/or implied final compensation or salary for this job opening. Determination of official compensation or salary relies on several different factors including, but not limited to: level of position, complexity of job responsibilities, geographic location, candidate’s scope of relevant work experience, educational background, certifications, contract-specific affordability, organizational requirements and alignment with local market data. Our compensation includes other indirect financial components designed to support employees’ total well-being, which should be considered when evaluating our competitive benefits package. These monetary benefits include medical insurance, life insurance, disability, paid time off, maternity/paternity leave, 401(k) company match, training/education reimbursements and other work/life programs. _____________________________________________________________________________________________________ IntelliGenesis is committed to providing equal opportunity to all employees and applicants for employment. The Company is an Equal Opportunity Employer (EOE), and as such, does not tolerate discrimination, retaliation, or harassment of its employees or applicants based upon race, color, religion, gender, sexual orientation, national origin, age, genetic information, disability, or any other protected characteristic under local, state, or federal law in any employment practice. Such employment practices include, but are not limited to: hiring, promotion, demotion, transfer, recruitment, or recruitment advertising, selection, disciplinary action layoff, termination, rates of pay, or other forms of compensation and selection of training. IntelliGenesis is committed to the fair and equal employment of individuals with disabilities. It is the Company’s policy to reasonably accommodate qualified individuals with disabilities unless the accommodation would impose an undue hardship on the organization. In accordance with the Americans with Disabilities Act (ADA) as amended, reasonable accommodations will be provided to qualified individuals with disabilities, when such accommodations are necessary, to enable them to perform the essential functions of their jobs or to enjoy the equal benefits and privileges of employment. This policy applies to all applicants for employment and all employees.

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