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Explainable Ai Jobs in California (NOW HIRING)

Keysight Technologies, Inc.

R&D Engineer

Calabasas, CA · On-site

Explainable AI & Diagnostic Analytics Goal: Make engineering models transparent, interpretable, and auditable. * Integrate Explainable AI (XAI) methods (e.g., SHAP, LIME, attention visualization, or ...

Keysight Technologies, Inc.

R&D Engineer

Calabasas, CA · On-site

Explainable AI & Diagnostic Analytics Goal: Make engineering models transparent, interpretable, and auditable. * Integrate Explainable AI (XAI) methods (e.g., SHAP, LIME, attention visualization, or ...

Intuit

Staff AI Scientist

Mountain View, CA

$10/hr

Proficient in NLP techniques, Explainable AI, and ML frameworks. * Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R. * Efficient in SQL ...

Intuit

Staff AI Scientist

Mountain View, CA · On-site

$209K - $283K/yr

Proficient in NLP techniques, Explainable AI, and ML frameworks. * Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R. * Efficient in SQL ...

Intuit

Staff AI Scientist

Mountain View, CA · On-site

$209K - $283K/yr

Proficient in NLP techniques, Explainable AI, and ML frameworks. * Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R. * Efficient in SQL ...

Intuit

Staff AI Scientist

Mountain View, CA · On-site

$209K - $283K/yr

Proficient in NLP techniques, Explainable AI, and ML frameworks. * Expertise in modern advanced analytical tools and programming languages such as Python, Scala, Java and/or R. * Efficient in SQL ...

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Explainable Ai information

What is the difference between Explainable Ai vs Data Scientist?

AspectExplainable AiData Scientist
CredentialsTypically requires knowledge of AI, machine learning, and data analysis; certifications like AI or ML courses are commonRequires degrees in computer science, statistics, or related fields; certifications in data analysis or machine learning are beneficial
Work EnvironmentWorks within AI development teams, focusing on model transparency and interpretabilityWorks across data analysis, model building, and business insights, often in research or corporate settings
Industry UsageUsed in AI development, healthcare, finance, and any field requiring transparent AI modelsApplied in tech, finance, healthcare, and research for data-driven decision making

Explainable Ai focuses on making AI models transparent and understandable, ensuring trust and compliance. Data Scientists develop and analyze models, often working with complex data. While both roles involve AI and data, Explainable Ai specialists emphasize interpretability, whereas Data Scientists focus on model creation and insights.

What are some of the typical challenges faced when working in Explainable AI and how do professionals address them?

Professionals in Explainable AI often encounter challenges such as balancing model accuracy with interpretability, translating complex model outputs into understandable insights for non-technical stakeholders, and ensuring transparency without compromising sensitive data. Addressing these issues typically involves using specialized tools and frameworks for visualization, collaborating closely with data scientists, domain experts, and business teams, and staying updated on the latest research in model interpretability. Continuous learning and open communication are key to overcoming these challenges and delivering AI solutions that are both effective and trustworthy.

What are the key skills and qualifications needed to thrive as an Explainable AI specialist?

To thrive as an Explainable AI specialist, you need a strong background in machine learning, data science, and statistics, typically with an advanced degree in computer science or a related field. Familiarity with frameworks such as TensorFlow, PyTorch, and libraries like LIME or SHAP, as well as experience in model interpretability tools, is essential. Strong analytical thinking, effective communication, and the ability to translate complex technical concepts for non-technical stakeholders are crucial soft skills. These capabilities ensure that AI models are transparent, trustworthy, and can be responsibly integrated into decision-making processes.

What is Explainable AI?

Explainable AI (XAI) refers to methods and techniques in artificial intelligence that make the results of AI models understandable and interpretable by humans. XAI aims to provide transparency into how AI systems make decisions, helping users trust and effectively manage AI applications. This is especially important in fields like healthcare, finance, and law, where understanding the reasoning behind AI-driven outcomes can be crucial for accountability and compliance. By making AI more transparent, XAI also helps identify and address biases or errors in AI systems.
What are popular job titles related to Explainable Ai jobs in California? For Explainable Ai jobs in California, the most frequently searched job titles are:
What job categories do people searching Explainable Ai jobs in California look for? The top searched job categories for Explainable Ai jobs in California are:
What cities in California are hiring for Explainable Ai jobs? Cities in California with the most Explainable Ai job openings:
Explainable Ai jobs near you
Infographic showing various Explainable Ai job openings in California as of August 2026, with employment types broken down into 76% Full Time, 19% Part Time, 2% Temporary, and 3% Contract. Highlights an 67% Physical, 4% Hybrid, and 29% Remote job distribution.

Head of AI & Machine Learning

Pivotal Solutions

San Francisco, CA • On-site

Full-time

Re-posted 29 days ago

Job description


As the Head of AI & Machine Learning, you will lead the development of transformative AI systems, leveraging generative AI and multi-agent architectures to deliver innovative solutions. Your work will focus on creating advanced, custom AI models that process complex, multi-modal data and provide actionable insights. Internally, you will enhance operational efficiency through AI-driven systems. Externally, you will redefine user experiences by delivering personalized, transparent, and accessible AI solutions.
Responsibilities
  • Lead the development and scaling of a multi-agent AI platform to deliver sophisticated, end-to-end solutions.
  • Enhance integration with foundation models while building custom AI capabilities tailored to specific needs.
  • Design and expand agent workflows to handle complex tasks and decision-making processes.
  • Develop specialized neural architectures optimized for domain-specific reasoning and decision-making.
  • Create purpose-built AI agents with capabilities beyond general-purpose models.
  • Engineer proprietary orchestration layers to enable seamless collaboration among AI agents.
  • Build advanced systems to extract insights from diverse data sources, including documents, market signals, and user inputs.
  • Design novel evaluation frameworks to measure performance, trust, and qualitative outcomes.
  • Implement intelligence loops to enable continuous knowledge accumulation from user interactions.
  • Create explainable AI decision pathways to ensure transparency for users and compliance with regulations.
  • Architect adaptive interfaces that evolve based on user behavior and preferences.
  • Design privacy-preserving AI systems to protect sensitive data while enabling personalization.
  • Implement regulatory compliance guardrails to ensure adherence to industry standards.
  • Collaborate with leadership, engineering, operations, and design teams to integrate AI systems into products.
  • Stay at the forefront of AI innovation, researching and applying breakthrough techniques.

Requirements
Qualifications
  • PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
  • 5+ years of experience in AI/ML research and development, preferably at leading AI labs or technology companies.
  • Expertise in large language models (LLMs), multi-agent systems, and AI orchestration.
  • Experience applying AI in high-stakes domains, such as finance or regulated industries, is highly valued.
  • Proven ability to design and implement agent-based systems for complex, real-world tasks.
  • Demonstrated success in building practical AI applications with transparency and explainability.
  • Proficiency in retrieval-augmented generation for knowledge-intensive applications.
  • Strong understanding of domain-specific data and decision-making processes.
  • Background in human-AI interaction design and explainable AI methodologies.
  • Experience building systems that learn from user feedback and improve over time.
  • Deep knowledge of privacy-preserving AI techniques for sensitive data.
  • Ability to translate business needs into robust AI architectures.
  • Expertise in balancing innovation with regulatory and compliance requirements.
  • Strong leadership and mentoring skills with experience guiding technical teams.
  • Excellent communication skills to collaborate with non-technical stakeholders.
  • Commitment to building accurate, reliable, and trustworthy AI systems.
  • Published research in AI/ML conferences or proven industry implementations.
  • Proficiency in Python and relevant ML frameworks and libraries (e.g., TensorFlow, PyTorch, Hugging Face).

Apply