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Data Annotation Engineer Jobs in San Jose, CA (NOW HIRING)

YO AI Labs

AI Data Scientist Expert - Remote

Palo Alto, CA · Remote

$100 - $200/hr

Prompt Engineering * AI Output Evaluation * Quality Assurance * Technical Documentation * Technical & Report Writing * Content Review & Editing * Data Annotation * Data Interpretation * Fact Checking

YO AI Labs

AI Data Science Expert - Remote

San Jose, CA · Remote

$100 - $200/hr

Prompt Engineering * AI Output Evaluation * Quality Assurance * Technical Documentation * Technical & Report Writing * Content Review & Editing * Data Annotation * Data Interpretation * Fact Checking

iMerit

Be Seen First

Video Data Annotator

San Jose, CA · Remote

$35/hr

Remote (US Only) iMerit - An EXL Company, is engaging Video Data Annotators to contribute to a customer's robotics-focused video annotation project. In this role, you'll apply your expertise to help ...

YO AI Labs

AI Data Science Expert - Remote

Palo Alto, CA · Remote

$100 - $200/hr

Prompt Engineering * AI Output Evaluation * Quality Assurance * Technical Documentation * Technical & Report Writing * Content Review & Editing * Data Annotation * Data Interpretation * Fact Checking

Specter

Data Operations Engineer

San Francisco, CA · On-site

$81K - $110K/yr

Role: Specter is hiring a data operations engineer to build our research data operation. This ... Build and maintain internal tooling for labelers, including annotation interfaces, task pipelines ...

General Motors

Staff ML Systems Engineer

Sunnyvale, CA · On-site

The Data Labeling Engineering team designs, builds, and operates hybrid human/machine data labeling ... Familiarity with data labeling/annotation platforms or tools used by large labeling workforces (e.g ...

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All JobsData Annotation Engineer JobsData Annotation Engineer Jobs in San Jose, CA

Data Annotation Engineer information

See San Jose, CA salary details

$60.4K

$172.8K

$230.9K

How much do data annotation engineer jobs pay per year?

As of Aug 31, 2026, the average yearly pay for data annotation engineer in San Jose, CA is $172,823.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,400.00 and $229,700.00 per year, depending on experience, location, and employer.

What is a data annotation engineer?

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 as a data annotation engineer?

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.

What is the salary of data annotation engineer?

The salary of a data annotation engineer typically ranges from $40,000 to $80,000 annually, depending on experience, location, and the complexity of annotation tasks. Entry-level positions may start lower, while experienced professionals with specialized skills in tools like Labelbox or CVAT can earn higher salaries.

What are popular job titles related to Data Annotation Engineer jobs in San Jose, CA?

For Data Annotation Engineer jobs in San Jose, CA, the most frequently searched job titles are:

What job categories do people searching Data Annotation Engineer jobs in San Jose, CA look for?

The top searched job categories for Data Annotation Engineer jobs in San Jose, CA are:

What cities near San Jose, CA are hiring for Data Annotation Engineer jobs?

Cities near San Jose, CA with the most Data Annotation Engineer job openings:

Data Annotation Engineer jobs near you
Infographic showing various Data Annotation Engineer job openings in San Jose, CA as of August 2026, with employment types broken down into 1% As Needed, 81% Full Time, 15% Part Time, and 3% Contract. Highlights an 84% Physical, 4% Hybrid, and 12% Remote job distribution, with an average salary of $172,823 per year, or $83.1 per hour.

Data Annotation Lead

Physical Intelligence

San Francisco, CA • On-site

Full-time

Re-posted 2 days ago

Job description

Physical Intelligence is bringing general-purpose AI into the physical world. We are a group of engineers, scientists, roboticists, and company builders developing foundation models and learning algorithms to power the robots of today and the physically-actuated devices of the future.
The role
We're looking for a Data Annotation Lead to own annotation operations and scale the team behind it. Annotation is core to how our models improve, and demand is growing fast. You will scale the annotation workforce from 100s to 1,000s while raising the quality bar - designing the org, the training pipeline, the quality system, and the metrics that let it scale efficiently.
You will own the people and the operation: throughput, quality, cost, and delivery across every annotation type.
In this role you will
  • Own annotation operations end-to-end: throughput, quality, cost, and on-time delivery across all annotation types.
  • Scale the annotation workforce from 100s to 1,000s: workforce planning, org design, and the hiring and onboarding funnel.
  • Build and lead a multi-layer management structure; hire, develop, and manage managers and team leads.
  • Scale throughput with autolabeling and model-based annotation: design human-in-the-loop workflows where models pre-label and annotators review, correct, and escalate, so output grows faster than headcount.
  • Stand up the training and certification pipeline that brings new annotators and teams to the quality bar quickly and consistently.
  • Define and continuously raise the quality bar: rubrics, calibration, audit/QA loops, and quality-adjusted productivity.
  • Establish operational metrics and reporting (presence, throughput, acceptance/rejection, rework) and drive week-over-week improvement.
  • Run capacity planning and prioritization against competing demand; allocate teams to the highest-impact work.
  • Manage performance at scale with clear standards, feedback, and a fair improvement/exit process.
  • Partner with product and engineering to define annotation tooling that unlocks throughput and quality.
  • Partner with research and project leads to translate annotation needs into clear instructions, rubrics, and SLAs.
  • Own the in-house vs. vendor mix and manage external partners where used.
  • Own the annotation operating budget and unit economics; improve cost-per-annotation while protecting quality.

What you'll bring
  • 7+ years leading scaled data or annotation operations, including teams in the 100s+.
  • 3+ years as a manager of managers.
  • Track record standing up 0→1 annotation programs.
  • Deep command of annotation best practices, operations, and strategy.
  • Experience integrating autolabeling and model-based annotation into human workflows; building human-in-the-loop pipelines that raise throughput without sacrificing quality.
  • Fluency with operational and quality metrics; data-driven management of large workforces.
  • Strong cross-functional partnership with product, engineering, and research/ML.
  • Clear written and verbal communication; able to set and hold standards across a large, distributed team.
  • Working understanding of ML and why annotation quality drives model performance.

Nice to have
  • Experience in robotics, autonomous vehicles, or frontier-AI data pipelines.
  • Experience managing distributed/global and/or vendor workforces.
  • Built annotation tooling or partnered tightly with a tooling team.
  • Experience training or fine-tuning autolabeling models, or partnering closely with the ML teams that do.

Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

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