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Adversarial Machine Learning Jobs in Maplewood, NJ

NOUS RESEARCH

Machine Learning Engineer, Evals

New York, NY · On-site

$153K/yr

Red teaming and adversarial eval experience * Familiarity with psychometrics or measurement theory HOW TO APPLY Email recruiting@nousresearch.com with the following information: * In the Subject: The ...

Arlo

Head of Data & Machine Learning

New York, NY · On-site

... as adversarial. Arlo is rebuilding health insurance for small businesses from first principles ... It allows for efficient training of large scale machine learning models, but it also has to serve ...

Arlo

Head of Data & Machine Learning

New York, NY · On-site

... as adversarial. Arlo is rebuilding health insurance for small businesses from first principles ... It allows for efficient training of large scale machine learning models, but it also has to serve ...

Hinge Health, Inc

Lead Security Engineer

New York, NY · On-site +1

$191K - $286K/yr

AI/ML security certifications or familiarity with adversarial machine learning threats and mitigation strategies * Experience building or integrating security controls into CI/CD pipelines and AI ...

Rain

Machine Learning Engineer - Fraud Risk

New York, NY · On-site

$170K - $240K/yr

Lead experimentation on model explainability, drift detection, and adversarial robustness for fraud ... Strong understanding of supervised/unsupervised learning, anomaly detection, and statistical ...

CACI

Senior AI/ML Research Engineer

Florham Park, NJ

$109K - $150K/yr

Apply machine learning, statistics, and best-of-breed AI capabilities to develop algorithms to ... Familiarity with concepts and techniques associated with adversarial AI and AI/ML assurance. Top ...

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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 2, 2026, the average hourly pay for adversarial machine learning in Maplewood, NJ is $21.57, according to ZipRecruiter salary data. Most workers in this role earn between $18.94 and $23.08 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 Maplewood, NJ look for?

The top searched job categories for Adversarial Machine Learning jobs in Maplewood, NJ are:

What cities near Maplewood, NJ are hiring for Adversarial Machine Learning jobs?

Cities near Maplewood, NJ with the most Adversarial Machine Learning job openings:

Adversarial Machine Learning jobs near you

Staff Machine Learning Engineer, Financial Connections

Stripe

New York, NY • On-site

Full-time

Posted 8 days ago

Job description

Who we are
About the team
Financial Connections is Stripe's open banking platform, enabling businesses to securely access consumer-permissioned financial data. Our platform connects to thousands of financial institutions, powering use cases from account verification to risk assessment to personal financial management. Across the Financial Connections Engineering org, we focus on delivering high-quality, enriched bank data at scale - building the ML systems that transform raw financial data into actionable signals for both internal Stripe teams and external merchants.
Our ML work spans transaction categorization, risk scoring, data enrichment, and the development of intelligent systems that improve data quality across our network. We operate at the intersection of fintech infrastructure and applied machine learning, solving problems that directly impact Stripe's ability to serve millions of businesses and consumers.
What you'll do
We're looking for machine learning engineers who want to build intelligent systems that provide financial data at scale. You'll play a key role in designing, training, and deploying ML models that improve the quality, accuracy, and usefulness of financial data across Stripe's ecosystem.
Responsibilities
  • Design, build, train, evaluate, deploy, and own ML models in production that improve transaction categorization, risk scoring, and data enrichment across Financial Connections
  • Design and build large-scale ML systems that operate on diverse financial data from thousands of institutions
  • Experiment and iterate on ML models (using tools such as PyTorch, TensorFlow, XGBoost) to achieve key business goals around data quality and accuracy
  • Develop pipelines and automated processes to train and evaluate models in offline and online environments
  • Integrate ML models into production systems and ensure their scalability and reliability
  • Collaborate with product, data science, and engineering partners across Stripe to identify opportunities where ML can improve outcomes for merchants and consumers
  • Engage with the latest ML/AI developments and take calculated risks in transforming innovative ideas into productionized solutions
  • Mentor engineers and contribute to a strong ML engineering culture within the team
Who you are
We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.
Minimum requirements
  • 10+ years of industry experience building and shipping ML systems in production
  • Proficient with ML libraries and frameworks such as PyTorch, TensorFlow, XGBoost, as well as Spark
  • Hands-on experience in designing, training, and evaluating machine learning models
  • Hands-on experience in productionizing and deploying models at scale
  • Hands-on experience in orchestrating data pipelines and efficiently leveraging large-scale datasets
  • Strong collaboration skills and the ability to work across teams and contribute to peers' success
  • Ability to thrive with a high level of autonomy and responsibility and an entrepreneurial mindset
Preferred qualifications
  • MS or PhD degree in ML/AI or a related field (e.g., math, physics, statistics, computer science)
  • Experience in fintech, open banking, or financial data domains
  • Experience with NLP, LLMs, or text classification at scale
  • Experience in adversarial or noisy-data domains such as fraud detection, risk modeling, or data quality
  • Proven track record of building and deploying ML systems that have effectively solved ambiguous business problems
  • Experience with deep learning architectures, including transformers

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