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Adversarial Machine Learning information

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How much do adversarial machine learning jobs pay per hour?

As of Sep 1, 2026, the average hourly pay for adversarial machine learning in Cambridge, MA is $23.31, according to ZipRecruiter salary data. Most workers in this role earn between $20.48 and $24.95 per hour, depending on experience, location, and employer.

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 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 job categories do people searching Adversarial Machine Learning jobs in Cambridge, MA look for?

The top searched job categories for Adversarial Machine Learning jobs in Cambridge, MA are:

What cities near Cambridge, MA are hiring for Adversarial Machine Learning jobs?

Cities near Cambridge, MA with the most Adversarial Machine Learning job openings:

Adversarial Machine Learning jobs near you
Infographic showing various Adversarial Machine Learning job openings in Cambridge, MA as of August 2026, with employment types broken down into 1% As Needed, 71% Full Time, 27% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $48,488 per year, or $23.3 per hour.

AI / Machine Learning Engineer with Security Clearance

Lexington, MA • On-site

John Galt Staffing
51 - 200 employees
Learn more about John Galt Staffingexternal

Other

Posted 5 days ago

Job description

The Artificial Intelligence Technology and Systems Group at MIT Lincoln Laboratory has over 50 years of experience developing revolutionary technologies for critical national missions. Group 52 AI Technology & Systems specializes in machine learning (ML) algorithms, technologies, and systems that extract and analyze information from multimedia data—including speech, text, images, and video. In recent years, we have significantly expanded our mission to include developing impactful ML solutions for cybersecurity in collaboration with the Nation’s top cyber organizations. As a recognized leader in both basic and applied AI/ML, our group is shaping emerging AI fields and driving the Laboratory’s efforts in AI Assurance across the Department of Defense and the Intelligence Community. We also integrate expertise in multimedia, cyber, and AI assurance to develop cutting-edge technologies for Operations in the Information Environment (OIE). A hallmark of our work is a focus on operational relevance: we design and evaluate AI/ML systems using realistic datasets and metrics, and we partner directly with intelligence analysts and cyber operators to ensure rapid transition of our technologies into real-world, mission-critical systems. Required Skills:
Knowledge of artificial intelligence ideally with applications on multimedia, cyber security or adversarial machine learning / AI security experience.
Applied AI/ML knowledge – solid graduate-level coursework or equivalent work experience applying machine-learning theory, deep learning, NLP, computer-vision, graph analytics, or adversarial/AI-assurance techniques.
Proficient Python programming – comfortable writing clean, modular, and testable code; expert-level use of deep-learning frameworks (PyTorch, TensorFlow, etc.), data-science stacks (NumPy, pandas, SciPy, etc.), and 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) is a plus.
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. Desired Skills:
Ability to read, evaluate, and implement state-of-the-art AI research papers.
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.

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