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Adversarial Machine Learning Jobs (NOW HIRING)

MIT Lincoln Laboratory

AI/ML Programmer

Lexington, MA · On-site

... adversarial machine learning / AI security experience • Advanced AI/ML knowledge - solid graduate-level coursework or equivalent work experience in machine-learning theory, deep learning, NLP ...

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 ...

Signifyd

Senior Machine Learning Engineer I // II

Denver, CO · On-site +1

$107K - $147K/yr

We seek equally motivated individuals who are passionate about keeping our customers safe while pulling the field of adversarial machine learning forward. The Role As a Senior Machine Learning ...

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How much do adversarial machine learning jobs pay per hour?

As of Aug 6, 2026, the average hourly pay for adversarial machine learning in the United States is $21.33, according to ZipRecruiter salary data. Most workers in this role earn between $18.75 and $22.84 per hour, depending on experience, location, and employer.

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.
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Adversarial Machine Learning jobs near you
Infographic showing various Adversarial Machine Learning job openings in the United States as of August 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 100% In-person job distribution, with an average salary of $44,363 per year, or $21.3 per hour.

AI/ML Programmer

MIT Lincoln Laboratory

Lexington, MA • On-site

Full-time

Re-posted 10 days ago

Job description

Job Summary:
MIT Lincoln Laboratory is a leading research institution focused on artificial intelligence and national security. They are seeking an AI/ML Programmer to design and develop advanced machine learning methods and algorithms, while contributing to impactful projects in cybersecurity and multimedia data analysis.
Responsibilities:
• Design, program, and architect advanced ML methods.
• Develop algorithms in speech, natural language processing, multimedia, cyber, and graph analytics.
• Publish and present research at premier conferences.
• Research on state-of-the-art algorithms in signal processing, natural language processing, graph analytics, or adversarial AI.
• Applying innovative methods to challenging, real-world problems.
• Working in a collaborative, team-oriented development environment.
Qualifications:
Required:
• M.S. in electrical engineering, computer science or other relevant discipline or a BS and 3 years of relevant experience
• Knowledge of artificial intelligence ideally with applications on multimedia, cyber security or adversarial machine learning / AI security experience
• Advanced AI/ML knowledge – solid graduate-level coursework or equivalent work experience in machine-learning theory, deep learning, NLP, computer-vision, graph analytics, or adversarial/AI-assurance techniques
• Ability to read, evaluate, and implement state-of-the-art research papers
• Proficient Python programming – comfortable writing clean, modular, and testable code
• Expert-level use of deep-learning frameworks (PyTorch, TensorFlow, etc.)
• Expert-level use of data-science stacks (NumPy, pandas, SciPy, etc.)
• Hands-on experience with agentic/LLM-oriented toolkits (e.g., MCP)
• Operating in a rapid research-to-prototype pipeline – demonstrated ability to take a cutting-edge research concept, design an experimental plan, implement a functional prototype, and iterate based on quantitative evaluation
• Experience with benchmark datasets and realistic metrics (e.g. latency, accuracy, robustness)
• Software-engineering best practices – strong Git workflow (branching, pull-requests, code reviews), continuous-integration testing, environment reproducibility (e.g., conda, virtualenv)
• Ability to document code, write reproducible experiment notebooks, and maintain versioned releases
• Problem-solving & analytical mindset – can decompose novel, ill-defined problems, propose multiple solution paths, and select the most promising approach based on empirical evidence
• Detail-oriented, able to multi-task, with the ability to work autonomously with minimal supervision
• Effective written and oral communication skills in technical environments, technical reports, internal wiki pages; delivers clear demo presentations and briefings to both technical audience and senior mission stakeholders
• Current TS/SCI clearance or willingness to obtain one
Company:
MIT Lincoln Laboratory is a federally funded research and development center chartered to apply advanced technology to problems of national security. Founded in 1951, the company is headquartered in Lexington, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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