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Adversarial Machine Learning Jobs in California (NOW HIRING)

PayPal

Sr Machine Learning Engineer

San Jose, CA · On-site

$159K - $236K/yr

This job will design, develop, and implement machine learning models and algorithms to solve ... adversarial conditions * Published research is a plus - but shipping code matters more than ...

PayPal, Inc.

Sr Machine Learning Engineer

San Jose, CA · On-site

$159K - $236K/yr

This job will design, develop, and implement machine learning models and algorithms to solve ... adversarial conditions * Published research is a plus - but shipping code matters more than ...

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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 California? For Adversarial Machine Learning jobs in California, the most frequently searched job titles are:
What job categories do people searching Adversarial Machine Learning jobs in California look for? The top searched job categories for Adversarial Machine Learning jobs in California are:
What cities in California are hiring for Adversarial Machine Learning jobs? Cities in California with the most Adversarial Machine Learning job openings:
Adversarial Machine Learning jobs near you
Infographic showing various Adversarial Machine Learning job openings in California as of July 2026, with employment types broken down into 78% Full Time, and 22% Contract. Highlights an 80% In-person, and 20% Remote job distribution.

Machine Learning Scientist - Vice President

JPMorganChase

Palo Alto, CA • On-site

Full-time

Re-posted 26 days ago

Job description

Job Summary:
JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers and businesses. As a Machine Learning Scientist - Vice President, you will lead the development of advanced ML solutions across various domains, including natural language processing and recommendation systems, while collaborating with cross-functional teams to deliver impactful AI capabilities.
Responsibilities:
• Lead and deploy state-of-the-art advanced machine learning systems across NLP, speech recognition, recommendation systems, and information retrieval.
• Design and build agentic AI systems for multi‑step workflows, including tool/function calling, multi‑agent orchestration, planning, grounding, and safety guardrails.
• Use reinforcement learning (policy optimization, bandits, RLHF‑style approaches where appropriate) to improve personalization, dialog policies, and sequential decision‑making systems.
• Fine-tune and adapt LLMs/SLMs using PEFT (LoRA, AdaLoRA, IA3), distillation, and quantization; optimize for quality, latency, cost, and production constraints.
• Select and innovate on ML strategies for various banking problems.
• Analyze and evaluate the ongoing performance of developed ML systems.
• Collaborate with multiple partner teams, such as Business, Technology, Product Management, Design, Analytics, and Model Governance to deploy solutions into production.
• Build domain understanding to identify high-impact opportunities, ensure responsible AI usage, and drive measurable outcomes (customer experience, automation, accuracy, and efficiency).
• Implement privacy, safety, and security controls for GenAI systems, including PCI handling/redaction, policy checks, jailbreak resistance, and auditability.
Qualifications:
Required:
• MS with 7+ years, or PhD with 4+ years of hand-on industry experience in building and deploying machine learning systems (NLP/Information Retrieval/Recommendation System and/or GenAI) in production environment
• Good understanding of the latest advancement of NLP concepts, such as the transformer architecture, knowledge distillation, transfer learning, and representation learning.
• Applied GenAI experience with LLMs and the ability to fine‑tune and deploy SLMs for targeted use cases, familiarity with prompt design, grounded generation, and RAG.
• Experience with scaling LLM systems (caching, batching, prompt/version governance, evaluation harnesses)
• Strong foundation in machine learning, deep learning, and statistical modelling, including model evaluation and error analysis.
• Solid understanding of Information Retrieval concepts (indexing, ranking, dense/sparse retrieval, re-ranking) and/or recommendation systems.
• Ability to design experiments — establish strong baselines, choose meaningful metrics, and evaluate model performance rigorously
• Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
• Proficiency in Python and common ML libraries (PyTorch/TensorFlow, Hugging Face, scikit-learn), and ability to write production-quality code.
• Ability to collaborate in cross-functional environments with product, engineering, and control partners.
• Solid written and spoken communication skills
Preferred:
• 5 years of hands-on experience with virtual assistant model development and optimization
• Experience orchestrating multi‑agent teams with supervisor agents, debate/consensus mechanisms, and role‑specialized toolkits for complex enterprise tasks.
• Building agent governance and eval suites: red‑teaming, adversarial tests, safety scorecards, regression suites for prompts/tools
• Experience with RL/bandits, preference optimization, or human feedback loops for personalization.
• Experience in regulated finance domains and working with risk/control processes.
• Experience with MLOps/LLMOps: CI/CD for models, monitoring/alerting, model versioning, evaluation of pipelines, and rollback strategies.
• Experience with A/B experimentation and data/metric-driven product development.
Company:
With a history tracing its roots to 1799 in New York City, JPMorganChase is one of the world's oldest, largest, and best-known financial institutions—carrying forth the innovative spirit of our heritage firms in global operations across 100 markets. Founded in 2000, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.

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