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Full Time Machine Learning Data Annotation Jobs in Phoenix, AZ

TSMC

Senior Data Engineer / Data Curator

Phoenix, AZ · On-site

$105K - $143K/yr

... machine learning or AI-driven environments. • Strong proficiency in Python (Pandas, NumPy) and ... annotation tools and platforms for manual or semi-automated labeling. • Experience with NLP data ...

TSMC

Senior Data Engineer / Data Curator

Phoenix, AZ · On-site

$130K - $177K/yr

... in machine learning or AI-driven environments. * Strong proficiency in Python (Pandas, NumPy) and ... Experience with data annotation tools and platforms for manual or semi-automated labeling.

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Full Time Machine Learning Data Annotation information

See Phoenix, AZ salary details

$37.2K

$121.9K

$195.1K

How much do full time machine learning data annotation jobs pay per year?

As of Jun 12, 2026, the average yearly pay for full time machine learning data annotation in Phoenix, AZ is $121,868.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,800.00 and $135,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Full Time Machine Learning Data Annotation Specialist, and why are they important?

To thrive as a Full Time Machine Learning Data Annotation Specialist, you need strong attention to detail, basic data literacy, and familiarity with data labeling concepts, often supported by a high school diploma or equivalent. Proficiency in specialized annotation platforms, spreadsheet tools, and sometimes knowledge of Python or labeling frameworks is typically required. Reliability, patience, and effective communication are valuable soft skills for ensuring accuracy and collaborating with team members. These skills and qualities are crucial because they directly impact the quality of training data, which is essential for developing effective machine learning models.

What are Full Time Machine Learning Data Annotation jobs?

Full time machine learning data annotation jobs involve labeling, tagging, or categorizing data such as images, text, audio, or video to help train machine learning models. Data annotators play a crucial role in ensuring that AI systems learn from high-quality, accurately labeled datasets. These positions often require attention to detail, consistency, and sometimes familiarity with the subject matter or specialized tools. Full-time roles may be remote or onsite and can span industries like autonomous vehicles, healthcare, retail, and more.

What are some common challenges faced by machine learning data annotators, and how are these typically addressed within a team?

Machine learning data annotators often encounter challenges such as maintaining consistency in labeling, handling ambiguous data, and meeting tight deadlines for large datasets. Teams usually address these by establishing clear annotation guidelines, conducting regular training sessions, and implementing quality assurance processes like peer reviews and spot checks. Collaboration with data scientists and project managers is also common, ensuring that annotators can ask questions and clarify uncertainties, leading to higher-quality labeled data and a supportive work environment.

What is the difference between Full Time Machine Learning Data Annotation vs Data Labeling Specialist?

AspectFull Time Machine Learning Data AnnotationData Labeling Specialist
CredentialsHigh school diploma or equivalent; some roles prefer technical certificationsHigh school diploma or equivalent; training often provided on the job
Work EnvironmentOffice or remote; collaborative with data science teamsRemote or office; focused on labeling tasks
Industry UsageUsed across AI/ML companies, tech firms, and startupsCommon in AI/ML, data services, and outsourcing companies
Job FocusCreating labeled datasets for machine learning modelsAnnotating data such as images, videos, or text for AI training

Full Time Machine Learning Data Annotation involves creating high-quality labeled datasets for AI models, often requiring technical understanding. Data Labeling Specialists focus on annotating data accurately, typically with less emphasis on technical skills. Both roles are essential in AI development but differ mainly in scope and technical complexity.

What are popular job titles related to Full Time Machine Learning Data Annotation jobs in Phoenix, AZ? For Full Time Machine Learning Data Annotation jobs in Phoenix, AZ, the most frequently searched job titles are:
What job categories do people searching Full Time Machine Learning Data Annotation jobs in Phoenix, AZ look for? The top searched job categories for Full Time Machine Learning Data Annotation jobs in Phoenix, AZ are:
Full Time Machine Learning Data Annotation jobs near you

Senior Data Engineer / Data Curator

TSMC

Phoenix, AZ • On-site

$105K - $143K/yr

Full-time

Posted 25 days ago

TSMC rating

8.2

Company rating: 8.2 out of 10

Based on 19 frontline employees who took The Breakroom Quiz

37th of 139 rated electronics manufacturers

Job description

Job Summary:
TSMC Arizona is a leading semiconductor manufacturing company, offering an opportunity to work at the most advanced fab in the United States. As a Senior Data Engineer in the AI Data Curation track, you will design and maintain scalable data pipelines, ensuring that the data for AI models is high-quality and aligned with ethical standards.
Responsibilities:
• Design and implement data pipelines for processing, cleaning, and curating large datasets used in model training and fine-tuning.
• Automate data cleaning processes (e.g., removing noise, duplicates, irrelevant content) and ensure datasets are appropriately labeled and structured.
• Collaborate with model teams to ensure data aligns with model requirements and performance goals.
• Assess and mitigate bias in datasets, ensuring that models are trained on diverse and representative data.
• Manage data storage and retrieval strategies, ensuring scalability and data consistency across different environments.
• Conduct regular audits to ensure data integrity, privacy, and security compliance.
Qualifications:
Required:
• Bachelor's degree in Computer Science, Data Science, or a related field.
• 5+ years of experience in data engineering, data wrangling, or data curation, particularly in machine learning or AI-driven environments.
• Strong proficiency in Python (Pandas, NumPy) and SQL for data manipulation and querying.
• Familiarity with cloud-based data storage (AWS S3, Google Cloud Storage, etc.) and distributed systems for managing large datasets.
• Experience with data annotation tools and platforms for manual or semi-automated labeling.
• Experience with NLP data formats, such as JSONL, text, or embeddings, and an understanding of tokenization.
• Experience managing data pipelines with tools like Apache Kafka, Apache Airflow, or similar ETL tools.
• Strong knowledge of AI ethics, data privacy, and compliance standards (GDPR, CCPA, etc.).
• Candidates must be willing and able to work on-site at our Phoenix Arizona facility.
• Communication
• Computer proficiency
• Presentation skills
• Listening
• Teamwork
Preferred:
• Experience with vector databases and indexing for LLMs (e.g., FAISS, Pinecone).
Company:
Established in 1987, TSMC is the world's first dedicated semiconductor foundry. Founded in 1987, the company is headquartered in Hsinchu, TWN, with a team of 10001+ employees. The company is currently Late Stage.

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