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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 Utah? For Explainable Ai jobs in Utah, the most frequently searched job titles are:
What job categories do people searching Explainable Ai jobs in Utah look for? The top searched job categories for Explainable Ai jobs in Utah are:
What cities in Utah are hiring for Explainable Ai jobs? Cities in Utah with the most Explainable Ai job openings:
Explainable Ai jobs near you
Infographic showing various Explainable Ai job openings in Utah as of July 2026, with employment types broken down into 79% Full Time, 19% Part Time, and 2% Contract. Highlights an 64% Physical, 3% Hybrid, and 33% Remote job distribution.

Senior Systems Engineer AI Code Metrics Daemon

Atlassian

Magna, UT • On-site, Remote

Full-time

Re-posted 20 days ago

Job description

Working at Atlassian

Atlassians can choose where they work whether in an office, from home, or a combination of the two. That way, Atlassians have more control over supporting their family, personal goals, and other priorities. We can hire people in any country where we have a legal entity.

We're building the observability layer for AI-assisted software development, measuring how much of an engineering org's code is actually written by AI coding agents, and attributing every line back to the work that produced it. You'll own major parts of a cross-platform endpoint agent end-to-end: architecture, implementation, and real-world deployment onto developer machines.


What You'll Do

  • Build the core systems that detect and attribute AI-generated code at the line level across real Git workflows (rebases, cherry-picks, resets, and all)

  • Design reliable pipelines for capturing, classifying, storing, and shipping developer + agent activity from the endpoint

  • Work directly with Git internals (reflog, commit identity across history rewrites, blob-level attribution) to collect data without interrupting developers

  • Build and maintain integrations with coding agents (Cursor, Claude Code, Copilot, Codex, Gemini CLI, and more) via editor/CLI hooks

  • Own cross-platform behavior across macOS, Linux, and Windows

What We're Looking For

  • Strong systems engineer backend, OS internals, endpoint/agent software, or developer infrastructure

  • We use Go, but it's fine if that's not your background (what matters is comfort with concurrent, long-running, low-level software)

  • Experience building production systems that must be fast, reliable, and invisible to the user

  • High-agency, high-responsibility individuals with strong product intuition (you care how developers experience what you build)

  • Excited about developer tools and using data to understand how engineers actually work with AI coding agents

  • Bonus: Git internals, developer tools / dev infra, endpoint or monitoring agents (EDR, sync clients, telemetry agents), SQLite or embedded storage, or cross-platform native development


At Atlassian, we strive to design equitable, explainable, and competitive compensation programs. We follow consistent hiring practices and account for each candidate's skills, knowledge, and experience when setting base pay within the range.

Please visit go.atlassian.com/payzones for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter.

This role may also be eligible for benefits, bonuses, commissions, and equity.


In The United States, we have three geographic pay zones. For this role, our current base pay ranges for new hires in each zone are:


Zone A: $180,000 - $235,000


Zone B: $162,000 - $211,500


Zone C: $149,400 - $195,050


Benefits & Perks

Atlassian offers a wide range of perks and benefits designed to support you, your family and to help you engage with your local community. Our offerings include health and wellbeing resources, paid volunteer days, and so much more. To learn more, visit go.atlassian.com/perksandbenefits.

About Atlassian

At Atlassian, we're motivated by a common goal: to unleash the potential of every team. Our software products help teams all over the planet and our solutions are designed for all types of work. Team collaboration through our tools makes what may be impossible alone, possible together.

We believe that the unique contributions of all Atlassians create our success. To ensure that our products and culture continue to incorporate everyone's perspectives and experience, we never discriminate based on race, religion, national origin, gender identity or expression, sexual orientation, age, or marital, veteran, or disability status. All your information will be kept confidential according to EEO guidelines.

To provide you the best experience, we can support with accommodations or adjustments at any stage of the recruitment process. Simply inform our Recruitment team during your conversation with them.

To learn more about our culture and hiring process, visit go.atlassian.com/crh.

In line with local law, identity verification (which may include use of biometric data) is a condition of employment with Atlassian for employment fraud purposes.

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