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Intern Ai Data Annotation Jobs in Michigan (NOW HIRING)

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Intern Ai Data Annotation information

What are the key skills and qualifications needed to thrive as an Intern AI Data Annotation Specialist, and why are they important?

To thrive as an Intern AI Data Annotation Specialist, you need strong attention to detail, basic data handling skills, and familiarity with common data formats, often supported by a background in computer science or related fields. Experience with data labeling tools, annotation platforms, and sometimes basic knowledge of Python or similar scripting languages is typically required. Reliability, patience, and effective communication are essential soft skills for ensuring accuracy and collaborating with team members. These competencies are crucial for generating high-quality annotated datasets that enable accurate machine learning model development.

What does a typical day look like for an AI Data Annotation Intern, and how do they collaborate with other teams?

As an AI Data Annotation Intern, your typical day involves labeling and categorizing data such as images, audio, or text to train machine learning models. You’ll use specialized annotation tools and follow detailed guidelines to ensure consistency and accuracy. Collaboration is key—you’ll often communicate with data scientists, machine learning engineers, and project managers to clarify requirements and provide feedback on ambiguous cases. This teamwork helps ensure the data you annotate aligns with project goals and quality standards, making your contributions vital to the development of AI solutions.

What does an Intern AI Data Annotation do?

An Intern AI Data Annotation is responsible for labeling and categorizing data, such as images, text, or audio, to help train artificial intelligence models. They ensure data is accurately tagged according to specific guidelines so that AI systems can learn to recognize patterns or make predictions. This role often involves using specialized annotation tools and requires attention to detail to maintain high-quality datasets. Interns may also assist with reviewing and correcting data, as well as collaborating with data scientists and engineers to improve annotation processes.
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:
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Intern Ai Data Annotation jobs near you

Senior Robotics Data Collection Engineer - Only W2

Saransh Inc

Warren, MI • On-site

$99K - $135K/yr

Contractor

Re-posted 10 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.