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Data Annotation For Ai Jobs in Michigan (NOW HIRING)

IntraEdge

AI Data Engineer

Detroit, MI

$113K - $136K/yr

The successful candidate will be responsible for designing, building, and maintaining the data infrastructure and pipelines that power our AI, machine learning (ML), agentic AI, and generative AI ...

Deloitte

Agentic AI, AI & Data Manager

Detroit, MI · On-site

... software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by ... Recruiting for this role ends on 7/18/2026 Work You'll Do You will lead and deliver engagements, or ...

Deloitte

Agentic AI, AI & Data Science Engineer

Detroit, MI

$113K - $136K/yr

Recruiting for this role ends on 7/31/2026. Work you'll do As an AI and Data Science Engineer III on the AI & Data team, you will be responsible for driving technology-focused client delivery across ...

micro1 AI

Legal AI Trainer - Remote

Detroit, MI · Remote

$90 - $130/hr

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

micro1 AI

Legal AI Trainer - Remote

Detroit, MI · Remote

$80 - $105/hr

Collaborate with product and research teams to refine data, guidelines, and best practices for AI-driven contract review solutions. Required Skills and Qualifications: * J.D. from an ABA-accredited ...

Compunnel

Data Scientist

Grand Rapids, MI · On-site

Job Summary We are seeking an experienced Data Scientist to lead and optimize the AI-driven product content ecosystem for owned brands. This role combines machine learning, product catalog management ...

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All JobsData Annotation For Ai JobsData Annotation For Ai Jobs in Michigan

Data Annotation For Ai information

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

AspectData Annotation For AiData Labeler
CredentialsBasic computer skills, attention to detailBasic computer skills, attention to detail
Work EnvironmentRemote or on-site, tech companies, AI projectsRemote or on-site, data processing companies
Industry UsageArtificial Intelligence, Machine LearningData management, content moderation
Job FocusPreparing data for AI algorithms through annotationLabeling data for various purposes, including AI

Data Annotation For Ai involves preparing datasets specifically for training AI models, focusing on detailed annotations. Data Labeler is a broader role that includes labeling data for multiple purposes, including AI but also other data management tasks. While both roles require similar skills, Data Annotation For Ai is more specialized towards AI development projects.

What is data annotation for AI?

Data annotation for AI is the process of labeling or tagging data—such as text, images, audio, or video—to make it understandable for machine learning models. Annotators add relevant information to raw data, helping AI systems learn to recognize patterns and make accurate predictions. This step is crucial for training, validating, and testing AI algorithms, especially in tasks like computer vision and natural language processing. High-quality data annotation directly impacts the effectiveness and reliability of AI applications.

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

To thrive as a Data Annotation Specialist for AI, you need a keen eye for detail, a solid understanding of data labeling concepts, and often a background in the relevant domain (such as language, images, or audio). Proficiency with annotation platforms, data management systems, and basic familiarity with tools like Excel or Python can be highly valuable. Strong communication, consistency, and time management skills help ensure accuracy and meet project deadlines. These abilities are crucial because high-quality, well-annotated data is foundational for training reliable and effective AI models.

What are some common challenges faced by data annotators working on AI projects, and how can they be addressed?

Data annotators for AI often encounter challenges such as maintaining consistency across large datasets, understanding ambiguous labeling instructions, and managing repetitive tasks. To address these issues, it's important to actively seek clarification on guidelines, participate in team discussions to align on labeling standards, and use annotation tools that flag inconsistencies. Regular feedback sessions with project leads also help improve accuracy and efficiency, fostering a collaborative and supportive work environment.
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Data Annotation For Ai jobs near you
Infographic showing various Data Annotation For Ai job openings in Michigan as of July 2026, with employment types broken down into 77% Full Time, 20% Part Time, and 3% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution.

Robotics Data Collection Engineer

Nastech Global

Warren, MI • On-site

Contractor

Re-posted 19 days ago

Job description

Position: Robotics Data Collection Engineer

Location: Warren, Michigan (Onsite)

Duration: 12+Months with possible extensions

Main Skills: Senior Robotics Data Collection Engineer (MLE, Python, Cloud exp, Linux)

Position Summary:

Join Automation, Robotics & Controls (ARC) AI team as a Robotics Data Collection Engineer. In this hands-on role, you will work directly with advanced robotic systems to collect, organize, and validate training data that enables AI-powered robotic manipulation in automotive manufacturing. You will contribute to building the datasets that power the next generation of intelligent manufacturing automation at Warren Technical Center.

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).
  • Ability to work on-site at Warren Technical Center, 5 days per week.
  • Attention to detail and ability to follow technical procedures and documentation.
  • Reliability, accountability, and ability to work independently and as part of a team.
  • 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.

What is Offered:

•              Hands-on experience with cutting-edge robotics and AI technology.

•              Opportunity to contribute to transformative manufacturing automation.

•              Collaborative team environment with world-class engineers and researchers.