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Day Data Annotation Jobs in Indiana (NOW HIRING)

Toptal

$99K - $136K/yr

Job Summary We are building a robust Data Annotation Platform designed to be the backbone of AI ... First 60 days * Weeks 1-2 (Onboarding): You'll get your hands dirty in the codebase and get up to ...

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Day Data Annotation information

Can I use ChatGPT for data annotation?

Day Data Annotation jobs typically involve labeling data manually to improve machine learning models. While ChatGPT can assist with generating or reviewing data, it is not a substitute for the detailed, accurate labeling performed by human annotators required in these roles.

Is it hard to get hired for data annotation?

Getting hired for a data annotation role generally depends on the employer's requirements, such as attention to detail and basic computer skills. Many positions are entry-level and may not require extensive experience or certifications, making them accessible to a wide range of applicants. However, competition can vary based on the company's demand and the complexity of the annotation tasks.

What are the key skills and qualifications needed to thrive as a Day Data Annotation Specialist, and why are they important?

To excel as a Day Data Annotation Specialist, you need strong attention to detail, data entry accuracy, and a solid understanding of the subject matter being annotated, often supported by a high school diploma or relevant experience. Familiarity with annotation tools, spreadsheets, and data management software is typically required. Excellent concentration, time management, and clear communication skills help professionals stand out in this role. These abilities are crucial to ensure high-quality, consistent data labeling that directly impacts the performance of machine learning models and downstream business applications.

What are Day Data Annotation jobs?

Day Data Annotation jobs involve reviewing and tagging data, such as images, text, audio, or video, during regular daytime hours. Annotators help prepare datasets for machine learning and artificial intelligence by labeling or categorizing information according to specific guidelines. This work is essential for training algorithms to recognize patterns, objects, or language. Day Data Annotation can be done remotely or in-office, and it often requires attention to detail and good communication skills.

What is the difference between Day Data Annotation vs Data Labeler?

AspectDay Data AnnotationData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, collaborative teamsRemote or on-site, independent work
Industry UsageAI/ML companies, tech firmsAI/ML, data processing companies
Job FocusAnnotating data for machine learning modelsLabeling data to train AI systems

Day Data Annotation and Data Labeler roles are similar, focusing on preparing data for AI. Day Data Annotation often involves more detailed annotation tasks, while Data Labelers may perform broader labeling activities. Both roles require basic technical skills and are vital in AI development across tech industries.

Does data annotation really pay you?

Data annotation jobs, including day data annotation roles, typically pay hourly or per task rates, with earnings varying based on experience, complexity of tasks, and platform. Many annotators earn a modest income, often comparable to entry-level work, and consistent pay depends on workload and employer policies.

Is data annotation real or fake?

Data annotation is a legitimate job involving labeling data such as images, text, or audio to train machine learning models. It requires attention to detail and familiarity with annotation tools, and it is widely used in AI development. The work is real and essential for creating accurate AI systems.

What are some common challenges faced by Day Data Annotation specialists and how can they be addressed?

Day Data Annotation specialists often encounter challenges such as maintaining high accuracy while handling repetitive tasks, interpreting ambiguous data, and meeting tight deadlines. To address these, it's important to develop strong attention to detail, use project guidelines as references, and communicate with team leads or peers when uncertainties arise. Many organizations also provide regular feedback and quality assurance checks, which help annotators improve their performance and consistency over time.
What are the most commonly searched types of Data Annotation jobs in Indiana? The most popular types of Data Annotation jobs in Indiana are:
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Day Data Annotation jobs near you
Talent Network: Senior Full stack Engineer (New Business)

Talent Network: Senior Full stack Engineer (New Business)

Toptal

Remote

$99K - $136K/yr

Full-time

Posted 21 days ago

Job description

About Toptal

Toptal is a global network of top talent in business, design, and technology that enables companies to scale their teams, on-demand. With $200+ million in annual revenue and team members based around the globe, Toptal is the world's largest fully remote workforce.

We take the best elements of virtual teams and combine them with a support structure that encourages innovation, social interaction, and fun. We see no borders, move at a fast pace, and are never afraid to break the mold.

Job Summary

We are building a robust Data Annotation Platform designed to be the backbone of AI development. Instead of clients managing their own labeling infrastructure, they use our platform to drop massive datasets-100k images, audio clips, PDFs, or video segments-and define the questions they need answered. Whether it's timestamping topics in a video or auditing subtitles, we build the tools that make high-quality data possible.

Our stack is focused heavily on Next.js, Node, and Supabase. We're looking for someone who lives and breathes the JS ecosystem and wants to solve the unique UI/UX and architectural challenges that come with handling diverse media at scale.

This is a remote position. We do not offer visa sponsorship or assistance. Resumes and communication must be submitted in English.

Responsibilities
  • Build & Scale: Design and maintain reliable Node & React (Next.js) code for a high-traffic data platform.
  • Technical Leadership: Own features from the whiteboard to production. You'll create proposals and architectural designs for new platform capabilities.
  • Collaborative Growth: Review code, mentor teammates, and contribute to a culture of constructive, transparent feedback.
  • Iterate: Experiment with new AI-driven tooling to improve team productivity and platform performance.
Qualifications and Job Requirements
  • Expert TypeScript/JS Skills: Deep knowledge of modern frontend and backend development with Node/React (Next).
  • Backend Fundamentals: Strong understanding of databases (PostgreSQL/Supabase), clean code, and testing.
  • Architectural Mindset: Ability to weigh implementation time against failure tolerance, security, and long-term maintenance.
  • Problem Solver: You don't just find bugs; you weigh multiple solutions and pick the best path forward.
  • Adaptability: Comfortable in a fast-paced environment with a long, ambitious roadmap.
Nice to Haves
  • Modern Styling & UI Consistency: Proficiency with Tailwind CSS and experience building or maintaining Design Systems to ensure a cohesive user experience across complex data workflows.
  • Python: Experience with Python for ML or LLM-related integrations is a bonus, but not required.
  • Deployment: Familiarity with Vercel and CI/CD pipelines.
  • Domain Interest: Experience with data labeling, media processing (video/audio), or PDF manipulation.
Engagement Highlights
  • Direct Impact: Every feature you build directly enables the next generation of AI models.
  • Innovation Freedom: We encourage exploring new tools and frameworks that make us faster and better.
  • Global Collaboration: Work with a highly skilled, remote-friendly team of professionals.
  • Complex Challenges: Solve high-level architectural problems involving large-scale data and complex user workflows.
  • Career Growth: Opportunities to expand front-end and back-end skills, and into a leadership position.
First 60 days
  • Weeks 1-2 (Onboarding): You'll get your hands dirty in the codebase and get up to speed with the team's workflow.
  • Weeks 3-8: You'll take the lead on a specific initiative. This isn't just taking tickets, you'll be responsible for architectural designs, feature proposals, and seeing a complex component through to completion.
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