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Freelance Ai Data Annotation Jobs in Michigan (NOW HIRING)

Saransh Inc

Senior Robotics Data Engineer - Only W2

Warren, MI · On-site

$99K - $135K/yr

... AI. · Familiarity with robotics simulation platforms (e.g., Isaac Sim) and synthetic data generation. · Experience with data labeling tools and annotation workflows at scale. · Hands-on knowledge ...

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Freelance Ai Data Annotation information

What does a freelance AI data annotator do?

A Freelance AI Data Annotation job involves labeling, tagging, or categorizing data to train artificial intelligence models. This can include tasks like identifying objects in images, transcribing audio, or classifying text. Annotators follow specific guidelines to ensure consistency and accuracy so that the AI can learn from high-quality, well-labeled datasets. These jobs are typically remote, flexible, and can be project-based, making them popular for freelancers. Attention to detail and the ability to follow instructions are key skills for success in this role.

What skills and qualifications are needed to be a freelance AI data annotator?

To thrive as a Freelance AI Data Annotator, you need strong attention to detail, basic computer literacy, and familiarity with data labeling concepts, often supported by a high school diploma or equivalent. Experience with annotation platforms, labeling tools, and sometimes knowledge of spreadsheet software or project management systems is typically required. Reliability, time management, and effective communication are standout soft skills for meeting project deadlines and collaborating with remote teams. These skills are crucial for ensuring high-quality, accurate data annotations that directly impact the performance of AI models.

What are common challenges faced by freelance AI data annotators, and how can they be addressed?

Freelance AI data annotators often encounter challenges such as maintaining consistency in labeling, meeting tight deadlines, and managing repetitive tasks. To address these, it's important to thoroughly understand the project guidelines, seek clarification from clients when needed, and use annotation tools efficiently. Regular communication with project managers and participating in quality checks can also help ensure accuracy and smooth workflow. Building a routine and taking short breaks can reduce fatigue and improve focus.

What is the difference between Freelance Ai Data Annotation vs Freelance Data Labeler?

AspectFreelance Ai Data AnnotationFreelance Data Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote, flexibleRemote, flexible
Industry UsageAI and machine learning projectsData organization and categorization
Job FocusAnnotating data for AI trainingLabeling data for various purposes

Freelance Ai Data Annotation involves preparing data specifically for AI models, often requiring understanding of annotation tools. Freelance Data Labeler may perform similar tasks but can include broader data labeling roles. Both roles are remote, flexible, and essential for data-driven industries, but Ai Data Annotation is more specialized for AI development projects.

What are the most commonly searched types of Ai Data Annotation jobs in Michigan?

The most popular types of Ai Data Annotation jobs in Michigan are:

What are popular job titles related to Freelance Ai Data Annotation jobs in Michigan?

For Freelance Ai Data Annotation jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Freelance Ai Data Annotation jobs in Michigan look for?

The top searched job categories for Freelance Ai Data Annotation jobs in Michigan are:

What cities in Michigan are hiring for Freelance Ai Data Annotation jobs?

Cities in Michigan with the most Freelance Ai Data Annotation job openings:

Freelance Ai Data Annotation jobs near you
Infographic showing various Freelance Ai Data Annotation job openings in Michigan as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 13% Part Time, 2% Temporary, 3% Contract, and 1% Nights. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution.

Senior Robotics Data Collection Engineer - Only W2

Saransh Inc

Warren, MI • On-site

$99K - $135K/yr

Contractor

Re-posted 21 days ago

Job description

Role: Senior Robotics Data Collection Engineer
Location: Warren, MI (Onsite from Day 1)
Job Type: W2 Contract
 
Main Skills: Senior Robotics Data Collection Engineer (MLE, Python, Cloud exp, Linux)
 
Key Responsibilities:
· Collect high-quality robot telemetry, sensor, and visual data from manufacturing robotic systems in lab and production-like environments.
· Operate and monitor robotic systems, GELLO teleop interfaces, and data collection hardware.
· Organize, label, and validate data according to established annotation guidelines and quality standards.
· Perform manual annotation and verification when necessary to generate high-quality ground truth labels.
· Execute data collection campaigns following documented protocols and experimental designs.
· Troubleshoot data collection issues and document problems for engineering teams.
· Collaborate with AI engineers, robotics engineers, and manufacturing teams to ensure data meets model training requirements.
 
Required Qualifications:
· College or bachelor’s degree in engineering (Mechanical Engineering or Electrical Engineering preferred).
· Attention to detail and ability to follow technical procedures and documentation.
· Strong, demonstrated hands-on experience operating, troubleshooting, and maintaining industrial or collaborative robotic arms.
· Proficiency in Linux environments and basic scripting (e.g., Python) to interface with robotic systems and manage data pipelines.
· Proven experience working directly with perception sensors and hardware, with a solid understanding of capturing and validating high-quality sensor data.
 
Preferred Qualifications:
· Experience with robotics, manufacturing, or data collection.
· Familiarity with Python, Linux, or data tools (beneficial but not required).
· Experience operating or troubleshooting technical equipment.
· Basic understanding of machine learning, AI, or data annotation concepts.
· Experience in automotive or manufacturing environments.