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Intern Data Annotation Tech Jobs in California (NOW HIRING)

Sesame

Technical Program Manager, Data

San Francisco, CA · On-site

$200K - $260K/yr

... annotation pipelines. Preferred Qualifications: * Understanding of ML data pipelines and their applications. * Experience working with LLMs. * Familiarity with data labeling for audio technologies ...

Intuitive

Mgr Clinical Data

Sunnyvale, CA · On-site

As a global leader in robotic-assisted surgery and minimally invasive care , our technologies-like ... You will build and scale a high-performing organization of internal annotation specialists and ...

Voxelcloud

Chief Technology Officer

Los Angeles, CA · On-site

As Voxelcloud's technology moves from early development and validation into full productization ... Optimize annotation data quality and label consistency for R&D in a cost-efficient way. o Work with ...

Voxelcloud

Chief Technology Officer

Los Angeles, CA · On-site

As Voxelcloud's technology moves from early development and validation into full productization ... Optimize annotation data quality and label consistency for R&D in a cost-efficient way. o Work with ...

Tapestry

Data Science Manager

Mountain View, CA

Annotation Rigor: Drive a comprehensive and scalable data annotation strategy that prioritizes ... PhD or Master's in a quantitative field and 8+ years of tech or energy industry work experience as ...

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All JobsIntern Data Annotation Tech JobsIntern Data Annotation Tech Jobs in California

Intern Data Annotation Tech information

Is it hard to get hired for data annotation?

Getting hired as a data annotation intern typically requires basic computer skills, attention to detail, and sometimes familiarity with annotation tools. The application process is often straightforward, with many companies offering entry-level positions that do not require extensive experience or certifications.

What are some common challenges faced by Intern Data Annotation Techs, and how can they overcome them?

Intern Data Annotation Techs often encounter challenges such as maintaining consistency in labeling large datasets and understanding nuanced instructions for annotation tasks. To overcome these hurdles, it's important to ask clarifying questions early on, regularly review annotation guidelines, and participate in team discussions about edge cases. Collaboration with more experienced annotators and feedback from supervisors also help in refining skills and ensuring high-quality data preparation. Developing attention to detail and adaptability will contribute to a successful internship experience.

Does data annotation tech really pay?

Data annotation technicians typically earn hourly wages that can range from minimum wage to above average, depending on experience and location. Entry-level roles often pay around $10 to $15 per hour, with higher rates for skilled annotators or those working on specialized projects. Compensation can also include flexible schedules and remote work options.

What is the difference between Intern Data Annotation Tech vs Intern Data Labeler?

AspectIntern Data Annotation TechIntern Data Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentData annotation platforms, remote or officeData labeling platforms, remote or office
Industry UsageAI, machine learning, data scienceAI, machine learning, data science
Job FocusAnnotating data for training AI modelsLabeling data for machine learning algorithms

Both roles involve preparing data for AI systems, with similar skills and work environments. The main difference lies in terminology; 'Data Annotation Tech' emphasizes technical annotation tasks, while 'Data Labeler' is a more general term. Both are entry-level positions vital for training AI models in the tech industry.

What is a data annotation intern?

A data annotation intern is a temporary position where individuals label or categorize data, such as images, text, or videos, to help train machine learning models. The role typically involves using annotation tools and requires attention to detail, with tasks often performed remotely or in a supervised environment.

What are Intern Data Annotation Techs?

Intern Data Annotation Techs are entry-level professionals, often students or recent graduates, who support machine learning projects by labeling and categorizing data, such as images, text, or audio. Their work is essential for training AI systems, as accurately annotated data helps algorithms learn to make correct predictions. These interns typically use specialized software tools to tag or classify data according to specific guidelines. The role requires attention to detail, consistency, and sometimes basic technical skills, depending on the complexity of the data and tasks. Internships in data annotation can provide valuable exposure to the fields of artificial intelligence and data science.

Can I do data annotation with no experience?

Intern Data Annotation Tech roles often do not require prior experience, as training is typically provided to teach basic annotation tools and guidelines. Basic computer skills and attention to detail are usually sufficient to start, making it accessible for beginners. Developing familiarity with annotation software and understanding data labeling standards can improve performance over time.

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

To thrive as an Intern Data Annotation Tech, you need attention to detail, basic data management skills, and familiarity with data labeling concepts, often supported by a high school diploma or ongoing college coursework. Experience with annotation platforms, spreadsheet tools, and sometimes basic scripting languages is helpful. Strong communication, reliability, and the ability to follow detailed instructions are valuable soft skills in this role. These abilities ensure accurate and efficient data labeling, which is critical for training reliable machine learning models.
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Intern Data Annotation Tech jobs near you

Senior Technical Program Manager (AI/ML)

campus4tech

Mountain View, CA • On-site

Full-time

Posted 16 days ago

Job description

Job Title- Senior Technical Program Manager (AI/ML)
Location- Mountain View, CA, United States, 94041
Reporting Type- Onsite
Duration: 11 Months
W2 candidates only for this role
Summary
The Client's R&D Operations Organization is seeking a highly motivated and technically skilled Technical Program Manager (TPM) to lead and oversee data annotation programs that power our cutting-edge AI research initiatives. This role sits at the intersection of program management, data operations, and AI/ML, and will play a pivotal part in ensuring that our data annotation efforts are scalable, high-quality, and aligned with the needs of our research and product teams.
You will collaborate closely with researchers, data scientists, ML engineers, and vendor operations to drive the end-to-end lifecycle of large-scale data labeling and curation efforts - from strategy and planning to execution, delivery, and quality evaluation.
Requirements
  • Bachelor's or Master's degree in a technical field (e.g. Computer Science, Data Science, Machine Learning, Information Systems) or equivalent practical experience.
  • 7+ years of experience in technical program management, project management, or operations in data-centric or AI/ML environments.
  • Strong understanding of ML development workflows, data pipelines, and annotation lifecycle.
  • Experience managing large-scale data labeling or data collection efforts, including working with third-party vendors.
  • Familiarity with big data platforms (e.g. Apache Spark, Databricks, Hadoop) and data warehousing concepts.
  • Excellent organizational, problem-solving, and communication skills with the ability to influence cross-functional stakeholders.
  • Proven track record of driving cross-functional teams to deliver complex technical projects on time and with high quality.
  • Excellent communication, negotiation and analytical skills, with the ability to document standard operating procedures and processes
  • Advanced working SQL Knowledge, Ability to build and maintain analytics to track, forecast, and visualize consumption through ad-hoc SQL, reports, and dashboards
  • Experience performing root cause analysis on internal and external data and processes to answer specific business questions and identify opportunities for improvement.
  • Self-motivated and able to work independently, as well as in a team environment.
  • Preferred good working knowledge of GPU technology and its applications in generative AI and machine learning.
  • Familiarity with big data technologies such as Apache Spark, Delta Lake, and MLflow is a plus.

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