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Contract Data Annotation Jobs in Dearborn, MI (NOW HIRING)

Contract Data Annotation information

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

AspectContract Data AnnotationData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, project-basedRemote or on-site, task-based
Industry UsageAI/ML training, tech companiesAI/ML training, tech companies
Job FocusAnnotating data for machine learning modelsLabeling data to improve AI algorithms

Contract Data Annotation involves completing specific annotation projects for AI training, often on a contractual basis. Data Labelers focus on labeling data to enhance machine learning models, typically performing similar tasks. Both roles require attention to detail and are used in AI/ML industries, but Contract Data Annotation emphasizes project-based work with defined deliverables.

What is a contract data annotation?

A contract data annotation job involves labeling or tagging data—such as images, text, audio, or video—according to specific guidelines, usually on a temporary or project-based contract. These annotations help train machine learning models by providing accurate, human-labeled examples for algorithms to learn from. Contract workers are typically hired for a set period or project and may work remotely or on-site, depending on the employer. The work requires attention to detail, adherence to quality standards, and sometimes familiarity with specialized annotation tools.

What are the key skills and qualifications needed to thrive as a contract data annotation specialist?

To thrive as a Contract Data Annotation Specialist, you need a keen eye for detail, strong analytical skills, and familiarity with data labeling standards, often supported by experience in data management or related fields. Proficiency with annotation platforms (such as Labelbox, Prodigy, or CVAT) and basic knowledge of data formats like JSON or XML are commonly required. Excellent communication, time management, and the ability to work independently help individuals excel in this often remote and deadline-driven role. These skills ensure high-quality, accurate data annotations that are vital for training reliable machine learning models.

What are some common challenges faced by contract data annotation professionals, and how can they be effectively managed?

Contract data annotation professionals often encounter challenges such as maintaining consistency in labeling, managing tight project deadlines, and ensuring data privacy. These challenges can be effectively managed by following detailed annotation guidelines, utilizing collaborative tools for team communication, and participating in regular quality assurance checks. Staying organized and proactive about seeking clarification from project leads also helps ensure high-quality, accurate results and a smooth workflow.
What job categories do people searching Contract Data Annotation jobs in Dearborn, MI look for? The top searched job categories for Contract Data Annotation jobs in Dearborn, MI are:
What cities near Dearborn, MI are hiring for Contract Data Annotation jobs? Cities near Dearborn, MI with the most Contract Data Annotation job openings:
Infographic showing various Contract Data Annotation job openings in Dearborn, MI as of June 2026, with employment types broken down into 3% As Needed, 53% Part Time, 41% Contract, and 3% Nights. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution.

Senior Robotics Data Collection Engineer - Only W2

Saransh Inc

Warren, MI • On-site

$99K - $135K/yr

Contractor

Re-posted 29 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.