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Annotation Tech Jobs in California (NOW HIRING)

Sesame

Technical Program Manager, Data

San Francisco, CA · On-site

$152K - $196K/yr

... annotation pipelines. Preferred : • Understanding of ML data pipelines and their applications. • Experience working with LLMs. • Familiarity with data labeling for audio technologies, such as ...

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Annotation Tech information

What are Annotation Techs?

Annotation Techs, short for Annotation Technicians, are professionals who label, categorize, and tag data—such as images, text, or audio—to help train machine learning models. Their work is critical in fields like artificial intelligence, where high-quality, accurately labeled data is needed to teach algorithms how to recognize patterns and make decisions. Annotation Techs may use specialized software tools to identify objects in images, transcribe speech, or classify pieces of text. Attention to detail and consistency are key skills in this role, as errors or inconsistencies can affect the performance of AI systems. These professionals often work in teams and may collaborate with data scientists and engineers to ensure data quality.

How hard is it to get hired by data annotation?

Getting hired as an annotation technician typically requires basic computer skills, attention to detail, and sometimes familiarity with annotation tools or platforms. Many positions are entry-level and do not require advanced education, making the hiring process relatively accessible, though competition can vary based on the employer and location.

What is the difference between Annotation Tech vs Data Labeler?

AspectAnnotation TechData Labeler
Required CredentialsHigh school diploma or equivalent; some roles may prefer technical certificationsHigh school diploma or equivalent; minimal certifications needed
Work EnvironmentOffice or remote; using specialized annotation toolsOffice or remote; using basic labeling software
Industry UsageAI, machine learning, autonomous vehicles, healthcareAI, machine learning, data preparation

Annotation Tech and Data Labeler roles often overlap in data preparation for AI projects. Annotation Tech typically involves more specialized tools and may require some technical knowledge, whereas Data Labelers focus on basic labeling tasks. Both roles are essential in training AI systems, but Annotation Tech positions often demand a deeper understanding of annotation processes and tools.

What does an annotation job do?

An annotation job involves labeling or tagging data, such as images, text, or videos, to help train machine learning models. Annotation technicians use specialized tools to add accurate labels, which are essential for developing AI systems, and often require attention to detail and knowledge of data privacy. The work is typically performed in a digital environment with flexible schedules and may require basic technical skills.

What are the key skills and qualifications needed to thrive as an Annotation Tech, and why are they important?

To thrive as an Annotation Tech, you need strong attention to detail, data labeling proficiency, and familiarity with data annotation guidelines, often supported by a background in computer science or related fields. Experience with annotation platforms such as Labelbox, Supervisely, or CVAT, and sometimes knowledge of basic scripting or data formats like JSON and XML, is typically required. Excellent communication, problem-solving skills, and the ability to follow complex instructions set top performers apart. These skills ensure high-quality, accurate data labeling that directly impacts the effectiveness of machine learning models.

Is annotation tech legit?

Annotation tech refers to roles involving labeling and annotating data for machine learning and AI training. These jobs are generally legitimate and often involve tasks like image, text, or audio annotation using specialized tools, with some positions offering flexible schedules and remote work. However, job seekers should verify the employer's reputation and avoid scams by researching the company before applying.

What are some common challenges faced by Annotation Techs when working with large datasets?

Annotation Techs often work with large and diverse datasets, which can present challenges such as maintaining consistency and accuracy across annotations, especially when dealing with ambiguous or complex data. Additionally, the repetitive nature of the work can lead to fatigue, making it important to stay focused and adhere to established guidelines. Collaboration with data scientists and project managers is crucial to clarify requirements and address any uncertainties, ensuring that the annotated data meets project standards and deadlines.

Does data annotation tech really pay?

Data annotation technicians typically earn hourly wages that range from minimum wage to around $15-$20 per hour, depending on experience, location, and the complexity of tasks. Some companies offer bonuses or pay increases for specialized skills or certifications, but overall, pay is generally modest compared to other tech roles.
What are popular job titles related to Annotation Tech jobs in California? For Annotation Tech jobs in California, the most frequently searched job titles are:
What job categories do people searching Annotation Tech jobs in California look for? The top searched job categories for Annotation Tech jobs in California are:
What cities in California are hiring for Annotation Tech jobs? Cities in California with the most Annotation Tech job openings:
Annotation Tech jobs near you
Infographic showing various Annotation Tech job openings in California as of July 2026, with employment types broken down into 4% Locum Tenens, 42% Full Time, 35% Part Time, 2% Contract, and 17% Nights. Highlights an 56% Physical, 1% Hybrid, and 43% Remote job distribution.

Human Data Operations Strategist

Encord

San Francisco, CA • On-site

Full-time

Medical, Dental, Vision, PTO

Posted 2 days ago

Job description

About us

Encord is the universal data layer for AI that helps 300+ AI teams train and run models on the right data. Our platform indexes, curates, annotates, and evaluates data across the full AI lifecycle, from development through production.

 

Trusted by Woven by Toyota, AXA, UiPath, Zipline, and more. We're an ambitious team of 100+ working at the frontier of AI and have raised $60M in Series C funding from Wellington Management, CRV, Next47 and Y Combinator.

 

The role

As a Human Data Operations Strategist, you will play a critical role in managing and optimising data annotation and machine learning workflows for our clients. You will work closely with cross-functional teams, including clients, annotation specialists, and machine learning engineers, to ensure high-quality data is available for AI models.

What you'll do

  • Oversee data annotation projects, translating complex AI and machine learning requirements into clear workflows and instructions for data annotation teams

  • Ensure the highest standards of data quality by designing and refining annotation processes, auditing results, and implementing feedback loops

  • Act as a trusted advisor to clients, helping them design and implement the best data annotation workflow for their human annotation process

  • Provide guidance and feedback to the annotation team, ensuring team members are equipped with the context and skills needed to perform high-quality work aligned with project requirements and best practices

  • Work closely with product and engineering teams to drive improvements in AI training data processes, tools, and methodologies

Who we're looking for

  • A sharp, execution-oriented operator with a consulting or AI company pedigree — you bring structured thinking, strong project management instincts, and a bias for getting things done

  • Analytically rigorous and comfortable with ambiguity — you break down complex operational challenges from first principles and build clear, actionable plans to solve them

  • Technically fluent enough to get hands-on with data — whether that's querying a database, auditing annotation outputs, or automating a workflow in Python

  • Passionate about AI and machine learning, with genuine curiosity about how data quality and operations underpin model performance

  • A natural communicator who can translate fluidly between ML engineers and non-technical clients, keeping complex multi-stakeholder projects on track

  • Entrepreneurial and collaborative — you thrive in fast-paced environments and take ownership without waiting to be told what to do

Experience requirements

  • 3–7 years of professional experience, with a strong preference for backgrounds in top-tier strategy consulting and/or operations or data roles at leading AI or technology companies

  • Proven ability to own complex, multi-stakeholder workflows end-to-end — from scoping and planning through execution, quality assurance, and iteration

  • Working proficiency in Python or SQL, with the ability to query data, automate workflows, or audit annotation outputs; broader familiarity with relational databases or data annotation tooling equally valued

  • Experience designing or optimising data operations processes with a strong eye for quality, consistency, and scalability — ideally in a context involving human-in-the-loop workflows or structured labelling tasks

  • Demonstrated ability to engage effectively with both technical stakeholders (ML engineers, data scientists) and non-technical clients, translating requirements clearly in both directions

  • Bonus: hands-on experience with computer vision, generative AI, or multimodal data workflows; prior exposure to data annotation platforms or quality management frameworks; experience coaching or managing operational teams

Why Encord

  • Competitive salary, commission, and meaningful equity in a high-growth start-up

  • Clear, accelerated growth opportunities as the company scales rapidly

  • Strong in-person culture: 4 days/week

  • Flexible PTO to fully recharge

  • Annual learning & development budget

  • Comprehensive health, dental, and vision coverage

  • Frequent travel opportunities across the U.S., London, and Europe

  • Bi-annual company offsites, twice-weekly team lunches, and monthly socials