Experience in data labeling, transcription review, AI development or working with AI data is a plus ... This is a remote / virtual position Physical Demands The physical demands described here are ...
Experience in data labeling, transcription review, AI development or working with AI data is a plus ... This is a remote / virtual position Physical Demands The physical demands described here are ...
AI ML Engineer
Indianapolis, IN · Remote
Woodland Hills, CA / Manson, OH / Dallas/Atlanta/New Jersey/Indianapolis/Cleveland/Chicago ( Remote ... Tagging and labeling workflows • Generative AI & LLMs o Prompt engineering for LLM-based ...
AI ML Engineer
Indianapolis, IN · Remote
Woodland Hills, CA / Manson, OH / Dallas/Atlanta/New Jersey/Indianapolis/Cleveland/Chicago ( Remote ... Tagging and labeling workflows • Generative AI & LLMs o Prompt engineering for LLM-based ...
Senior AI/ML Engineer
Indianapolis, IN · On-site +1
$99.90K - $137.20K/yr
Remote/Hybrid: This role is based remotely but if you live within a 50-mile radius of Sunnyvale, CA ... The Data Labeling Engineering team designs, builds, and operates hybrid human/machine data labeling ...
Senior AI/ML Engineer
Indianapolis, IN · On-site +1
$99.90K - $137.20K/yr
Remote/Hybrid: This role is based remotely but if you live within a 50-mile radius of Sunnyvale, CA ... The Data Labeling Engineering team designs, builds, and operates hybrid human/machine data labeling ...
$99.80K - $136.70K/yr
... remote workforce. We take the best elements of virtual teams and combine them with a support ... AI development. Instead of clients managing their own labeling infrastructure, they use our ...
$99.80K - $136.70K/yr
... remote workforce. We take the best elements of virtual teams and combine them with a support ... AI development. Instead of clients managing their own labeling infrastructure, they use our ...
This is a remote position. We do not offer visa sponsorship or assistance. Resumes and ... Generative AI applications, especially evaluation, structured outputs, semantic labeling ...
This is a remote position. We do not offer visa sponsorship or assistance. Resumes and ... Generative AI applications, especially evaluation, structured outputs, semantic labeling ...
Remote Ai Labeling information
What are the key skills and qualifications needed to thrive as a Remote AI Labeling Specialist, and why are they important?
To thrive as a Remote AI Labeling Specialist, you need strong attention to detail, basic computer literacy, and familiarity with data annotation principles, often supported by a high school diploma or equivalent. Experience with labeling platforms, image or text annotation tools, and sometimes knowledge of data privacy standards are typically required. Being reliable, self-motivated, and able to communicate clearly helps you consistently deliver accurate work and meet deadlines. These skills ensure high-quality labeled data, which is crucial for training effective AI models.
What are some common challenges faced by remote AI labeling specialists, and how can they be addressed?
Remote AI labeling specialists often encounter challenges such as maintaining focus during repetitive tasks, ensuring high accuracy, and managing tight deadlines. To address these, it's helpful to establish a structured work routine, use productivity tools to minimize distractions, and regularly review the labeling guidelines to avoid errors. Additionally, many companies provide collaborative platforms and regular feedback sessions, which can help clarify expectations and improve overall performance.
What is remote AI labeling?
Remote AI labeling is a job where individuals annotate or tag data—such as images, videos, text, or audio—from a remote location, typically their home. This labeled data is used to train artificial intelligence and machine learning models to recognize patterns or make decisions. Tasks may include drawing bounding boxes around objects in photos, transcribing audio, or categorizing content. Remote AI labeling jobs are popular for their flexibility and are essential in industries like autonomous vehicles, healthcare, and e-commerce. No advanced technical skills are usually required, though attention to detail is important.
What is the difference between Remote Ai Labeling vs Remote Data Annotation?
| Aspect | Remote Ai Labeling | Remote Data Annotation |
|---|---|---|
| Credentials | Basic computer skills, attention to detail | Basic computer skills, attention to detail |
| Work Environment | Remote, flexible hours | Remote, flexible hours |
| Industry Usage | AI development, machine learning | Data processing, machine learning |
| Job Focus | Labeling data specifically for AI models | Annotating various data types for machine learning |
Remote Ai Labeling and Remote Data Annotation are closely related roles in the AI industry. Both involve working remotely with data, but Ai Labeling focuses specifically on labeling data to train AI models, while Data Annotation encompasses a broader range of data types and tasks. Understanding these differences helps job seekers find roles that match their skills and career goals.
What are popular job titles related to Remote Ai Labeling jobs in Indiana? For Remote Ai Labeling jobs in Indiana, the most frequently searched job titles are:
What job categories do people searching Remote Ai Labeling jobs in Indiana look for? The top searched job categories for Remote Ai Labeling jobs in Indiana are:
What cities in Indiana are hiring for Remote Ai Labeling jobs? Cities in Indiana with the most Remote Ai Labeling job openings:
Other
Posted 13 days ago
Job description
Position Summary
The Data Labeling Specialist is responsible for carefully reviewing healthcare-related conversations to determine whether a safety event has occurred. This role is highly transactional and relies on consistently applying a well-defined rubric to ensure accurate and objective identification of safety-related concerns. Your work is critical in supporting healthcare clients in maintaining compliance and improving outcomes. This role requires high accuracy and attention to detail while labeling high volumes of patient conversations.
Key Responsibilities
- Conversation Review: Evaluate a high volume of healthcare-related conversations, using established rubrics to determine the presence or absence of safety events.
- Rubric Adherence: Apply clearly defined labeling criteria with consistency and discipline to ensure reliability in safety event detection.
- Quality and Accuracy: Deliver precise and objective reviews that align with expectations for labeling accuracy, supporting broader data integrity.
- Team Collaboration: Participate in calibration efforts with teammates to promote labeling alignment across the team.
- Feedback Adoption: Incorporate feedback from audits and performance checks to continually refine review accuracy and consistency.
Success Criteria:
- Consistently apply rubric criteria to produce high-quality, objective safety labels validated through regular audits.
- Achieve strong alignment with team standards and calibration practices.
- Meet or exceed performance targets for daily and weekly review volumes while maintaining quality benchmarks.
- Demonstrate reliability and consistency in handling high-volume, repetitive work while maintaining accuracy.
Key Skills and Abilities:
- Objectivity: Strong ability to apply standards without bias or interpretation, even under repetitive conditions.
- Attention to Detail: Exceptional focus on minute details to ensure safety flags are accurately identified.
- Rubric-Driven Thinking: Comfort and discipline in working within a structured rubric-based decision framework.
- Repetition Tolerance: High tolerance for performing repetitive tasks at scale while maintaining focus and precision.
- Critical Thinking: Ability to assess edge cases within rubric guidelines to make consistent, sound judgments.
- Quality Mindset: Motivated by accuracy and the importance of contributing to patient safety through diligence and care.
- Written Communication: Capable of documenting decisions clearly and concisely when necessary.
Qualifications
- 1-3 years of experience in customer support, health care, compliance, quality assurance or similar fields.
- Strong analytical and critical thinking skills.
- Ability to perform repetitive tasks with high attention to detail.
- Experience in data labeling, transcription review, AI development or working with AI data is a plus.
- Experience with pharmacovigilance is especially welcome.
Work Environment
The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job.
- This is a remote / virtual position
Physical Demands
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. While performing the duties of this job, the employee:
- Is regularly required to sit and use hands to type and operate a computer and phone
- Is frequently required to talk and hear
- Is occasionally required to stand and walk
- Must occasionally lift and/or move up to 25 pounds
- Is occasionally required to reach with hands and arms, stoop, kneel, or crouch
Other
- Time Management: Ability to manage time efficiently, balancing multiple projects and meeting tight deadlines.
- Team Collaboration: Experience working cross-functionally with other teams (e.g., AI, data science) to achieve shared goals.
- Problem-Solving: Strong problem-solving skills to identify inconsistencies or issues in labeling and find effective solutions.
- Adaptability: Ability to adapt to changing priorities and project requirements.
- Written Communication: Strong written communication skills to clearly document rubrics and provide detailed feedback during audits.
- Technical Aptitude: Familiarity with data labeling software, spreadsheets, or other data management tools.