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Explainable Ai Jobs in California (NOW HIRING)

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 ...

Advantest

Applied AI Engineer

San Jose, CA · On-site

Ensure AI solutions follow ethical, responsible and explainable AI practices Required Qualifications /Skills * 1-3 years of hands-on software engineering applied ML, optimization, robotics planning ...

Zania, Inc

Applied AI Engineer

Palo Alto, CA · On-site

$180K - $250K/yr

Develop an explainable AI system for risk assessment, allowing auditors and executives to understand and trust the AI's reasoning on high-stakes decisions. * Build an advanced RAG pipeline over a ...

Intuit

Staff AI Scientist

Mountain View, CA

$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 ...

Munich Re

Technical Architect - Data, Analytics & AI

Chico, CA · Hybrid

$64.75 - $83.50/hr

Collaborate with Data Science, Engineering, Security, and Risk teams to enable scalable, secure, and explainable AI solutions . * Establish architectural patterns for AI model deployment, monitoring ...

Advantest

Applied AI Engineer

San Jose, CA

Ensure AI solutions follow ethical, responsible and explainable AI practices RequiredQualifications /Skills * 1-3years of hands-on software engineeringapplied ML, optimization, robotics planning, EDA ...

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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.

Staff AI Scientist

Intuit

Mountain View, CA • On-site

$209K - $283K/yr

Full-time

Re-posted 5 days ago

Intuit rating

8.2

Company rating: 8.2 out of 10

Based on 92 frontline employees who took The Breakroom Quiz

106th of 244 rated software companies

Job description

Intuit is looking for innovative and hands-on Staff AI Scientist to join the GTM Tech platform AI team.

Come join our collaborative and creative group of AI scientists and machine learning engineers and build models that directly affect hundreds of thousands of our customers. In this role you will be building and deploying machine learning models using both analytical algorithms and deep learning approaches, targeted towards areas like marketing audiences, brand management, generative engine optimization, price optimization, creative generation and optimization, Digital twins etc. The world of marketing is changing dramatically. We are waiting for you to join us and do the best work of your life.


Responsibilities

  • Practices leadership and communication skills to influence teams and to evangelize AI science across the organization

  • Collaborates with stakeholders to define success criteria and align model metrics with business goals. Works side-by-side with product managers, software engineers, and designers in designing experiments and minimum viable products

  • Leads technical work of a scrum team: initiating and designing model solutions, driving end-to-end architecture designs of the team's work, and holding the team accountable for high quality code, git, design, costs and implementation standards

  • Performs hands-on data analysis and modeling with large data sets, including discovering data sources, getting data access, cleaning up data, and making them "model-ready". You need to be willing and able to do your own ETL and design/build featurization. 

  • Applies data mining, NLP, and machine learning (such as supervised/unsupervised, Causal-ML, Online Learning, Bayesian Learning, Reinforcement Learning, or Deep Learning) to real-world problems and datasets. 

  • Runs A/B tests to draw conclusions on the impact of your team's work and communicates results to peers and leaders

  • Communicates with partners to ensure successful delivery and integration of DS solutions. 

  • Proactively researches, explores, and enables new ML technologies. Keeps up with the new developments in academia and industry and considers possible extensions to solve Intuit customer problems. 


Qualifications


    • 4+ years of industry experience with AI science
    • BS, MS or PhD in Statistics, Mathematics, Computer Science, Economics, Operations Research, or equivalent
    • 4+ years of hands-on expertise in ML paradigms such as Causal-ML, supervised/unsupervised, Online, Bayesian, Reinforcement or Deep Learning.
    • Prior experience / qualifications in Marketing platforms, media management. If you have a background in Cognitive sciences / Human Psychology (Consumers, Buyers, Supporters, Detractors, Group behavioral sciences), this is the role for you. 
    • Proficient in multiple optimization paradigms such as combinatorial optimization, gradient methods, or Bayesian optimization.
    • 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, Hive, SparkSQL, etc.
    • Comfortable working in a Linux environment
    • Experience with building end-to-end reusable pipelines from data acquisition to model output delivery
    • Quick learner, adaptable, with the ability to work independently in a fast-paced environment
    • Strong oral and written communication skills. Ability to conduct meetings and make professional presentations, and to explain complex concepts and technical material to non-technical users

Footer

Intuit provides a competitive compensation package with a strong pay for performance rewards approach. This position will be eligible for a cash bonus, equity rewards and benefits, in accordance with our applicable plans and programs (see more about our compensation and benefits at Intuit: Careers | Benefits). Pay offered is based on factors such as job-related knowledge, skills, experience, and work location. To drive ongoing fair pay for employees, Intuit conducts regular comparisons across categories of ethnicity and gender.

The expected base pay range for this position is:
Mountain View $209,500 - $283,500
Employment Type: Full-Time

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