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Data Annotation Manager Jobs in Phoenix, AZ (NOW HIRING)

TSMC

Senior Data Engineer / Data Curator

Phoenix, AZ · On-site

$105K - $143K/yr

... 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 ...

General Motors

Senior AI/ML Engineer

Phoenix, AZ · On-site +1

$103K - $142K/yr

... data-annotation pipelines and machine-led training data solutions at foundation-model scale . We partner closely across AI/ML engineers , Product Operations , Product Management , Data Science , and ...

TSMC

Senior Data Engineer / Data Curator

Phoenix, AZ

$130K - $177K/yr

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 ...

TSMC

Senior Data Engineer / Data Curator

Phoenix, AZ · On-site

$130K - $177K/yr

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 ...

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Data Annotation Manager information

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$30.8K

$96.5K

$170.8K

How much do data annotation manager jobs pay per year?

As of Jun 16, 2026, the average yearly pay for data annotation manager in Phoenix, AZ is $96,456.00, according to ZipRecruiter salary data. Most workers in this role earn between $65,500.00 and $124,600.00 per year, depending on experience, location, and employer.

What is the salary of data annotation manager?

The salary of a Data Annotation Manager typically ranges from $60,000 to $120,000 annually, depending on experience, location, and company size. Senior roles or those in high-cost areas may offer higher compensation, and proficiency with annotation tools and team management can influence pay levels.

Is data annotation high paying?

Data annotation managers typically earn higher salaries than entry-level annotators due to their supervisory responsibilities and expertise in labeling tools and processes. Salaries vary based on experience, location, and company size, but the role generally offers competitive pay within the data labeling industry.

Is data annotation real or fake?

Data annotation is a real and essential process in machine learning and AI development, involving labeling data such as images, text, or audio to train algorithms. Data annotation managers oversee this work, ensuring accuracy and quality using tools like labeling platforms and quality control procedures.

What are some common challenges faced by Data Annotation Managers, and how can they be addressed?

Data Annotation Managers often encounter challenges such as maintaining high annotation quality across large and diverse datasets, managing a distributed team of annotators, and meeting tight project deadlines. To address these, it's important to implement robust quality assurance processes, provide ongoing training for annotators, and establish clear communication channels. Leveraging annotation tools with built-in validation features can also help ensure consistency and accuracy. Building a positive and collaborative team environment further contributes to better outcomes and workflow efficiency.

What does a Data Annotation Manager do?

A Data Annotation Manager oversees the process of labeling and categorizing data used to train machine learning models. They manage teams of annotators, ensure data quality, develop annotation guidelines, and coordinate with data scientists to meet project requirements. Their role is critical in maintaining high standards of accuracy and efficiency, as well as ensuring that datasets are properly prepared for AI and machine learning applications.

Is it hard to get a job with data annotation?

Securing a job as a data annotation manager typically requires experience in data labeling, familiarity with annotation tools, and understanding of data quality standards. While entry-level roles may be accessible with basic skills, advancing to managerial positions often demands relevant experience and leadership abilities.

What are the key skills and qualifications needed to thrive as a Data Annotation Manager, and why are they important?

To thrive as a Data Annotation Manager, you need expertise in data labeling processes, quality control, and a solid understanding of machine learning concepts, usually backed by a degree in computer science or a related field. Proficiency with annotation tools such as Labelbox, Supervisely, or CVAT, as well as experience with project management systems, is commonly required. Exceptional leadership, attention to detail, and strong communication skills help manage teams and ensure high annotation accuracy. These skills are critical for delivering reliable labeled datasets, which are essential for building effective AI and machine learning models.

What is the difference between Data Annotation Manager vs Data Labeling Specialist?

AspectData Annotation ManagerData Labeling Specialist
CredentialsBachelor's degree in related field, experience in data managementHigh school diploma or equivalent, training in labeling tools
Work EnvironmentTeam management, project oversight, collaboration with data scientistsHands-on labeling work, using annotation tools, focused on data tagging
Industry UsageUsed in AI/ML projects for overseeing annotation teamsPerforms the actual data labeling tasks in machine learning workflows

The Data Annotation Manager oversees the entire annotation process, managing teams and ensuring quality, while the Data Labeling Specialist focuses on executing labeling tasks. Both roles are essential in AI/ML data preparation but differ in responsibilities and scope.

What are the most commonly searched types of Data Annotation jobs in Phoenix, AZ? The most popular types of Data Annotation jobs in Phoenix, AZ are:
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What cities near Phoenix, AZ are hiring for Data Annotation Manager jobs? Cities near Phoenix, AZ with the most Data Annotation Manager job openings:
Data Annotation Manager jobs near you

Senior Data Engineer / Data Curator

TSMC

Phoenix, AZ • On-site

$105K - $143K/yr

Full-time

Posted 29 days ago

TSMC rating

8.2

Company rating: 8.2 out of 10

Based on 19 frontline employees who took The Breakroom Quiz

36th 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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