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Entry Level Ai Data Labeling Jobs in Arizona (NOW HIRING)

Data Scientist

Scottsdale, AZ · On-site

$80K - $120K/yr

Develop and deploy AI/ML models that power key products such as: * Procedure Appropriateness ... Explore entity extraction, rule-based labeling, embedding-based features, etc. Visualizations ...

... of AI and high-performance computing. Responsibilities Lead a small team of technicians (DCT I-V ... labeling, terminations -- and correct mistakes in the field immediately Ensure all team members ...

Navy Nuke - EE / ME CxA Agents

Phoenix, AZ · On-site

$84K - $106K/yr

... AI data projects for data center projects. They help their customers by providing solutions for ... We are looking for a Navy Nuke - EMN, MMN, or ETN to transition into an entry level CxA engineer ...

Route Operator Restocker

Mesa, AZ · On-site

$17 - $25/hr

Phoenix / Mesa, AZ Job Type: Full Time / Part Time Level: Entry Level Compensation: Competitive ... You are responsible for the consistent and professional restocking of up to 30 Ai Smart Coolers on ...

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Senior Associate ... PwC does not intend to hire experienced or entry level job seekers who will need, now or in the ...

Cabling Associate

Phoenix, AZ

$20.50 - $25.75/hr

... AI and hyperscale data centers across the U.S. This hybrid role blends field leadership with hands ... Perform on-site QA checks for fiber/copper terminations, labeling, routing, and test results while ...

... sensitivity labels) SharePoint Administration * Administer SharePoint Online sites, hubs ... Optimize prompts, grounding data sources, and AI responses for accuracy and relevance * Ensure ...

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Entry Level Ai Data Labeling information

What are some common challenges faced by entry-level AI data labelers, and how can they be addressed?

Entry-level AI data labelers often encounter challenges such as repetitive tasks, maintaining high accuracy under tight deadlines, and understanding complex labeling guidelines. To address these, it's important to take regular breaks to avoid fatigue, seek clarification from team leads when instructions are unclear, and leverage training resources provided by the company. Collaborating with peers and utilizing feedback can also improve efficiency and accuracy, making the role both manageable and rewarding for those starting their careers in AI.

What is the difference between Entry Level Ai Data Labeling vs Data Annotation Specialist?

AspectEntry Level Ai Data LabelingData Annotation Specialist
CredentialsBasic computer skills, no formal certification often requiredSimilar; often no formal certification, but some roles prefer training in data management
Work EnvironmentRemote or on-site, flexible hours, task-basedRemote or on-site, similar flexible environment
Industry UsageCommon in AI/ML companies, tech startupsUsed across tech, healthcare, automotive industries
Search/Comparison IntentHigh overlap, both involve labeling data for AI

Entry Level Ai Data Labeling and Data Annotation Specialist roles both involve labeling data to train AI models. While they share similar credentials and work environments, the term "Data Annotation Specialist" is often used interchangeably but may imply a broader scope or more specialized tasks. Both roles are essential in AI development and typically require minimal formal education, focusing on accuracy and attention to detail.

What is entry level AI data labeling?

Entry level AI data labeling involves tagging, categorizing, or annotating data such as images, text, audio, or video to help train artificial intelligence models. Data labelers follow specific guidelines to ensure the data is accurately marked, which is essential for machine learning algorithms to learn and make predictions. This role usually requires attention to detail, basic computer skills, and the ability to follow instructions, but typically does not require advanced technical experience. Data labeling is foundational to the development of reliable AI systems.

What are the key skills and qualifications needed to thrive as an Entry Level AI Data Labeler, and why are they important?

To thrive as an Entry Level AI Data Labeler, you need strong attention to detail, basic computer literacy, and the ability to follow specific instructions, typically with at least a high school diploma or equivalent. Familiarity with annotation tools, data labeling platforms, and sometimes spreadsheet software is commonly required. Reliability, focus, and effective communication are important soft skills that help ensure high-quality, consistent work. These skills and qualities are vital for producing accurate datasets that directly impact the performance and reliability of AI models.
What are the most commonly searched types of Ai Data Labeling jobs in Arizona? The most popular types of Ai Data Labeling jobs in Arizona are:
What are popular job titles related to Entry Level Ai Data Labeling jobs in Arizona? For Entry Level Ai Data Labeling jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Entry Level Ai Data Labeling jobs in Arizona look for? The top searched job categories for Entry Level Ai Data Labeling jobs in Arizona are:
What cities in Arizona are hiring for Entry Level Ai Data Labeling jobs? Cities in Arizona with the most Entry Level Ai Data Labeling job openings:
Junior Machine Learning Engineer-remote/AI/Data scientist

Junior Machine Learning Engineer-remote/AI/Data scientist

SynergisticIT

Phoenix, AZ • On-site

Other

Posted 12 days ago


Job description

Turn a Tech Layoff or a Career Gap Into a Reset for a Better Career or Laid Off in Tech? Rebuild Momentum With a Placement Process or Returning to Tech After a Break? Worried About a Gap? A layoff or a Career Gap can shake your confidence—even if you did nothing wrong. Downsizing, reorganizations, and budget cuts are business decisions, not personal failures. The tech industry still needs skilled developers — you just need the right platform to re-enter. A career gap doesn’t disqualify you — outdated skills do. But the job market can still feel brutal: you apply daily, watch automated rejections roll in, and wonder why your experience isn’t translating into interviews. The truth is that hiring has shifted. Employers want candidates who match current stacks, show recent hands-on proof, and interview strongly. If you’ve been out for 3–6+ months, that gap can become an extra filter—unless you deliberately rebuild momentum. We’re actively engaging candidates for full-time opportunities aligned to client needs: software programming, Java full stack development, Java/Python roles, DevOps engineering, and data roles spanning analytics, engineering, science, and ML/AI. Our primary focus remains Java/Full Stack/DevOps and Data/Engineering/Analytics/ML. SynergisticIT since 2010 has helped candidates land full-time roles at major organizations (examples often listed include Google, Apple, PayPal, Visa, Western Union, Wells Fargo, Intel, JPMC, Citi, Bank of America, Wayfair, and others), with offers in the $95k–$154k range depending on role and stack. Why laid-off candidates often struggle (even with experience) After a layoff, two things happen: Your skills may be solid, but your keywords and tools may be slightly behind the market. Your interview performance may drop because stress makes you second-guess. Also, employers increasingly expect hybrid capability: not just “I coded,” but “I can build + deploy + collaborate + document + explain.” That’s especially true for Java full stack, DevOps, data engineering, and ML/AI. What roles are commonly in demand right now Laid-off candidates often do best targeting roles that map to consistent enterprise demand. The main lanes include: Entry-level to mid-level software engineering roles (especially backend/full stack) Java full stack roles (enterprise stability) Java/Python developer roles (flexibility across teams) DevOps/Cloud roles (automation, pipelines, reliability) Data roles (analytics → engineering → ML/AI) why placement support matters rebuild a job-ready portfolio fast adjust your resume and LinkedIn for ATS practice interviews under real conditions get scheduled interviews through structured outreach A layoff recovery plan that actually works A smart recovery plan is not “apply more.” It’s: Re-stack: align skills to today’s demand (Java/full stack/devops or data/ML). Rebuild proof: projects that look like work, not homework. Rehearse interviews: DSA, system design, SQL, behavioral storytelling. Re-enter pipelines: structured outreach that leads to scheduled interviews. If you follow that with consistent coaching and iteration, your layoff becomes a pivot point—not a pause. If you’re ready to stop refreshing job boards and start rebuilding momentum with support, begin here: If you want to explore here are the key links: Event videos (OCW, JavaOne, Gartner): USA Today feature Discover JOPP: Job Placement Program Contact form:https://www.synergisticit.com/contact-us/ Please read our blogs Why do Tech Companies not Hire recent Computer Science Graduates | SynergisticIT What Recruiters Look for in Junior Developers | SynergisticIT Software engineering or Data Science as a career? Layoff reality: It can happen to anyone. Career recovery is a strategy problem, not a worth problem. In tech, it’s not only what you know—it’s how you position it and who guides you that determines how quickly you return stronger. Please note: Resume databases are shared with clients and interested clients will reach out directly if they find a qualified candidate for their req. Resume submissions may be shared with our JOPP team database also. Please unsubscribe if contacted or if you don’t want to be contacted please don’t submit your resume.