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Internship Data Labeling Jobs in California (NOW HIRING)

During your internship, you'll work directly with Canva's AI team on a live, industry-scale project ... eval/labelling, research, and product teams to determine whether and how synthetic data can ...

During your internship, you'll work directly with Canva's AI team on a live, industry-scale project ... eval/labelling, research, and product teams to determine whether and how synthetic data can ...

Join us for an internship with Giant Music, an independent record label built by the Azoff Company ... Data science background/majors preferred or combined interest/experience in A&R. Research and ...

New

Join us for an internship with Giant Music, an independent record label built by the Azoff Company ... Data science background/majors preferred or combined interest/experience in A&R. - Research and ...

Join us for an internship with Giant Music, an independent record label built by the Azoff Company ... Data science background/majors preferred or combined interest/experience in A&R. Research and ...

Join us for an internship with Giant Music, an independent record label built by the Azoff Company ... Data science background/majors preferred or combined interest/experience in A&R. Research and ...

New

Join us for an internship with Giant Music, an independent record label built by the Azoff Company ... Data science background/majors preferred or combined interest/experience in A&R. - Research and ...

New

Engineering Internship

Los Angeles, CA · On-site

$18 - $23.50/hr

... labeling, and tracking * Support cylinder coupon workflows, including sample preparation ... Help prepare samples and data packages for external testing, vendor evaluation, partner validation ...

As an independent music label and distribution arm, hello82 is rapidly expanding alongside the ever ... Support reporting and post-campaign wrap-ups by gathering data, assets, and learnings. * Assist in ...

Showing results 41-60

Internship Data Labeling information

What is an internship in data labeling?

An internship in data labeling involves assisting in the process of categorizing and annotating data, such as images, text, or audio, to help train machine learning models. Interns typically use specialized software to tag or classify data according to specific guidelines provided by the company or research team. This role is crucial for developing accurate artificial intelligence systems, as high-quality labeled data improves model performance. Data labeling internships are a great way to gain practical experience in the AI and machine learning field and learn more about data preprocessing workflows.

What types of tasks can I expect to handle daily as a data labeling intern?

As a Data Labeling Intern, your daily tasks typically include reviewing and accurately tagging data such as images, audio, or text according to specific guidelines provided by the company or project. You may use specialized annotation tools and work closely with data scientists, engineers, or QA teams to ensure high-quality, consistent labeling. Attention to detail is crucial, as your work directly impacts the performance of machine learning models. You might also participate in periodic team meetings to discuss challenges, clarify instructions, and receive feedback to improve labeling accuracy.

What are the key skills and qualifications needed to thrive as a data labeling intern, and why are they important?

To excel in an Internship Data Labeling role, you need strong attention to detail, basic analytical skills, and proficiency with data entry or spreadsheet tools, often supported by a high school diploma or current university enrollment. Familiarity with labeling platforms, annotation tools, and sometimes basic programming or database systems is beneficial. Reliability, communication, and the ability to follow instructions precisely are key soft skills for standing out in this position. These skills ensure data accuracy and consistency, which are crucial for developing reliable AI and machine learning models.

What is the difference between Internship Data Labeling vs Data Labeling Specialist?

AspectInternship Data LabelingData Labeling Specialist
CredentialsTypically students or entry-level with basic skillsRelevant experience or certifications in data annotation
Work EnvironmentInternship programs, often in tech or AI companiesFull-time or freelance roles in data annotation firms or tech companies
Industry UsageUsed as training or entry-level positionProfessional role with ongoing responsibilities
Search & Comparison IntentUnderstanding entry-level opportunities and trainingClarifying professional roles and career progression

Internship Data Labeling is an entry-level position designed for students or beginners gaining experience in data annotation. In contrast, Data Labeling Specialist is a professional role requiring prior experience or certifications, with more responsibility and independence. Internships serve as training grounds, while specialists handle ongoing data labeling tasks in a professional setting.

What are the most commonly searched types of Data Labeling jobs in California?

The most popular types of Data Labeling jobs in California are:

What job categories do people searching Internship Data Labeling jobs in California look for?

The top searched job categories for Internship Data Labeling jobs in California are:

What cities in California are hiring for Internship Data Labeling jobs?

Cities in California with the most Internship Data Labeling job openings:

PhD Research Scientist Intern

Canva

San Francisco, CA

$157K/yr

Full-time, Internship

Posted 16 days ago


Job description

Job Description

Join the team redefining how the world experiences design.

Hey, g'day, mabuhay, kia ora, , hallo, vitejte!

We're looking for current PhD students ready to bring their research into the real world and help shape the culture of AI at Canva.
Our full-time, 16 week AI Research Internship starts in September. During your internship, you'll work directly with Canva's AI team on a live, industry-scale project, turning part of your PhD journey into real world impact.
You'll gain hands on experience with real data, production infrastructure and real deadlines, while learning from and working alongside the researchers and engineerings creating Canva's next generation of AI-powered experiences.

Where and how you can work

Our global HQ is in Sydney, Australia, and as we continue to grow in our largest global market, we've made our way from down under to San Francisco.
Our Jackson Square campus blends the neighbourhoods vibrant downtown charm with Canva's signature flair. The space is designed for both deep work and collaboration, along with spaces for Canvanauts to relax, recharge and connect.
This role is based in San Francisco, and we're looking for someone who calls it home. Our hybrid way of working gives you flexibility - you'll have the option to work from home as well as connecting and collaborating with your team in-person, on campus. We trust teams to choose the balance that empowers them to achieve their goals.
What you'd be doing in this role

As Canva scales change continues to be part of our DNA. But we like to think that's all part of the fun. So this will give you the flavour of the type of things you'll be working on when you start, but this will likely evolve.

At the moment, this role is focused on:

  • Developing a synthetic data pipeline that produces conversations with our design agent - user turns, tool calls, and design-state changes - conditioned on personas and statistical criteria on requests derived from real usage.

  • Combining persona-conditioned user-simulation techniques (e.g. evolved/adversarial personas, dual-control agent frameworks) with our existing intent-labeling and clustering pipeline, to keep synthetic output in-distribution with real usage.

  • Measuring realism gap, distributional fit, and downstream evaluation quality (separability and agreement against our existing ELO rankings) with rigorous, reproducible validation.

  • Collaborating with the eval/labelling, research, and product teams to determine whether and how synthetic data can substitute for real user-generated content in evaluation.

  • Contributing methodology and findings back to the broader research community through publication where results support it - this is an active, largely unsolved area of research.

You're probably a match if you have:

  • You're currently completing a PhD (ideally third year or later).

  • You have experience with human-AI interaction, conversational agents, or user-modeling research.

  • You can design, run, and interpret machine-learning experiments with strong scientific rigour.

  • You can develop research code for data processing, model training, and evaluation.

  • You communicate technical work clearly in writing and presentations.

  • You enjoy collaborating with researchers and engineers on technically challenging problems.

  • You're a clear communicator who can collaborate effectively across teams.

At Canva, we're building a future powered by AI that's as magical as it is impactful. As a Generative AI Research Scientist Intern at Canva, you'll advance the frontier of agentic AI-building autonomous systems that can reason, plan, use tools, and act on behalf of hundreds of millions of users. You'll explore the boundaries of what agents can achieve in real-world productivity and creative scenarios, and bring that research into Canva's core product experiences-across design, presentations, documents, spreadsheets, video, and other multimodal creation and collaboration workflows.

Other stuff to know

We make hiring decisions based on your experience, skills and passion, as well as how you can enhance Canva and our culture. When you apply, please tell us the pronouns you use and any reasonable adjustments you may need during the interview process.
At Canva, we value fairness, and we strive to provide competitive, market-informed compensation whilst ensuring internal equity within the team in each region. The salary for this position is fixed at $157,000. When calculating offers, we make salary decisions based on market data, your experience levels, and internal benchmarks of your peers in the same domain and job level.

Please note that interviews are conducted virtually.