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Data Annotation Engineer Jobs in California (NOW HIRING)

Comrise

CA · On-site

$26 - $29/wk

Programming: Program parts using 2D and/or 3D toolpaths with Surfcam or Mastercam on mills and lathes. * Blueprint Reading: Read and interpret prints, specification sheets, and 3D files to determine ...

Abaka AI

Data Acquisition Engineer

Mountain View, CA · On-site

$136.30K - $163.60K/yr

As a Data Acquisition Engineer, you will own and scale the raw data supply ecosystem by combining ... data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA ...

Applied Intuition

Data Quality Specialist

Sunnyvale, CA

$30 - $45/hr

... annotation and labeling to support machine learning model training * Document quality standards and create comprehensive reports on data quality metrics * Collaborate with engineering teams to ...

Abaka AI

Data Acquisition Engineer

Mountain View, CA · On-site

The Data Acquisition Engineer will own and scale the raw data supply ecosystem, combining technical ... data annotation, and OTS datasets. Founded in 2021, the company is headquartered in Palo Alto, USA ...

Figure

Helix AI Engineer, Data Infrastructure

San Jose, CA · On-site

$126K - $165.20K/yr

They are seeking an experienced Data Infrastructure Engineer to enhance their AI data ... data annotation and dataset management tools. Company : Figure is an AI robotics company that ...

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Data Annotation Engineer information

See California salary details

$50.8K

$145.5K

$194.4K

How much do data annotation engineer jobs pay per year?

As of May 30, 2026, the average yearly pay for data annotation engineer in California is $145,530.00, according to ZipRecruiter salary data. Most workers in this role earn between $82,900.00 and $193,400.00 per year, depending on experience, location, and employer.

What is a Data Annotation Engineer job?

A Data Annotation Engineer is responsible for labeling and annotating data—such as text, images, audio, or video—to train machine learning models. They ensure that data is accurately categorized and structured to improve model performance. This role often involves using specialized annotation tools, following detailed guidelines, and working closely with data scientists and AI teams. Data Annotation Engineers play a crucial role in the development of AI applications by providing high-quality labeled datasets for supervised learning.

What are the key skills and qualifications needed to thrive in the Data Annotation Engineer position, and why are they important?

To thrive as a Data Annotation Engineer, you need a strong background in data analysis, attention to detail, and familiarity with annotation processes, often supported by a degree in computer science or a related field. Proficiency with annotation tools like Labelbox, CVAT, or VIA, and understanding of data formats used in machine learning, is commonly required. Excellent communication, collaboration, and organizational skills help you effectively manage projects and cooperate with cross-functional teams. These abilities are crucial for delivering high-quality labeled data, which directly impacts the performance of AI and machine learning models.

What are the main challenges faced by Data Annotation Engineers in their daily work?

One of the main challenges Data Annotation Engineers face is ensuring consistent accuracy and quality in labeling large and often complex datasets. Attention to detail is critical, as even small errors can significantly affect machine learning model performance. Additionally, engineers must frequently adapt to evolving annotation guidelines and emerging data types, which requires ongoing learning and flexibility. Collaboration with data scientists and project managers is common to clarify requirements and resolve ambiguities, making strong communication skills essential for success.
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Data Annotation Engineer jobs near you
Infographic showing various Data Annotation Engineer job openings in California as of May 2026, with employment types broken down into 33% Full Time, and 67% Contract. Highlights an 100% In-person job distribution, with an average salary of $145,530 per year, or $70 per hour.
Senior Manager of Quality Assurance, AIML Data Operations

Senior Manager of Quality Assurance, AIML Data Operations

Apple

Cupertino, CA • On-site

Full-time

Posted 15 days ago

Apple rating

8.1

Company rating: 8.1 out of 10

Based on 661 frontline employees who took The Breakroom Quiz

6th of 30 rated technology retailers

Job description

Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services, and customer experiences - quickly. ..The AIML team is looking for a passionate, detail-obsessed Senior Manager of Quality Assurance to lead the QA function within our Data Annotation operations. This is a rare opportunity to directly shape the quality standards that underpin the intelligent systems used by hundreds of millions of people every day...You will lead a team of QA professionals, define and defend data quality standards, and champion a culture of rigorous, scalable quality assurance across global annotation workflows. If you thrive at the intersection of operational excellence, data quality, and cross-functional leadership - this role was built for you.
As Senior Manager of QA for Data Annotation, you will own the end-to-end quality assurance strategy for annotation pipelines that feed directly into Apple's AI and machine learning models. You will partner closely with Data Science, Engineering, and Operations leadership to ensure that data quality is not an afterthought - it is a foundation.You will manage and develop a team of QA specialists and leads, set clear quality metrics, and build scalable processes that grow with our annotation programs. Your decisions will have measurable, real-world impact on the performance of Apple Intelligence products.
Bachelor's degree in a relevant field (Computer Science, Linguistics, Data Science, Operations, or equivalent)8+ years of experience in quality assurance, data operations, or a related field10+ years of people management experience leading QA or data operations teamsDemonstrated experience defining and operating QA programs for data annotation or content labeling at scaleSolid understanding of AIML concepts, with practical knowledge of how data quality affects model performanceStrong analytical skills with experience using data to measure, communicate, and drive quality improvementsProfessional fluency in English; excellent written and verbal communication skills across all levels of an organizationAbility to travel internationally when required
Master's degree or advanced certification in a relevant disciplineExperience with annotation platforms and QA tooling (e.g., Label Studio, Scale AI, Surge, Toloka, or similar)Familiarity with inter-annotator agreement methodologies (Cohen's Kappa, Krippendorff's Alpha, etc.)Experience managing QA for multilingual or multimodal annotation datasetsTrack record of building or scaling QA programs in a globally distributed, vendor-augmented operating modelPassion for Apple Intelligence products and a deep appreciation for the role of data quality in user experienceExperience with statistical sampling techniques and quality auditing frameworksFamiliarity with RLHF (Reinforcement Learning from Human Feedback) data workflows

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About Apple

Sourced by ZipRecruiter

Imagine what you could do here! At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Dynamic, intelligent people and inspiring, innovative technologies are the norm here. The people who work here have reinvented entire industries with all Apple Hardware products. The same real passion for innovation that goes into our products also applies to our practices strengthening our dedication to leave the world better than we found it.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Cupertino, CA, US

Year founded

1976

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