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Data Labeling Jobs in Michigan (NOW HIRING)

Saransh Inc

Senior Robotics Data Engineer - Only W2

Warren, MI · On-site

$99K - $135K/yr

... data labeling workflows to minimize manual annotation costs. · Enable simulation-to-real (Sim2Real) data workflows, including domain randomization and synthetic data generation. · Manage data ...

Ford Motor Company

Labels Supervisor

Dearborn, MI · On-site

Your leadership will empower our engineering teams to deliver compliant, high-quality labeling solutions and robust data tracking that our manufacturing plants and customers rely on. You will ...

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Data Labeling information

What are the key skills and qualifications needed to thrive in the Data Labeling position, and why are they important?

To thrive in Data Labeling, you need meticulous attention to detail, strong analytical abilities, and basic computer literacy, often supported by a high school diploma or equivalent. Familiarity with data annotation tools, image or text editing software, and experience with platforms like Labelbox or Amazon SageMaker Ground Truth are commonly advantageous. Exceptional concentration, patience, and the ability to follow precise instructions are valuable soft skills in this position. These skills and qualities are essential for ensuring the accuracy and consistency of labeled datasets, which are critical for training reliable AI and machine learning models.

What is a Data Labeling job?

A Data Labeling job involves annotating or tagging data, such as images, text, audio, or videos, to help train machine learning models. Labelers follow specific guidelines to classify data accurately so that AI systems can learn patterns and make predictions. This role is essential in fields like computer vision, natural language processing, and speech recognition. Strong attention to detail and consistency are crucial for ensuring high-quality training datasets.

What are the typical day-to-day responsibilities of a Data Labeling professional?

A Data Labeling professional is primarily responsible for reviewing and accurately tagging images, text, audio, or video data according to specified guidelines. Daily tasks often include managing large datasets, using annotation software to classify data, and verifying the quality and accuracy of the labels. Collaboration with data scientists, project managers, and other annotators is common, especially when clarifying labeling guidelines or resolving ambiguities. Attention to detail is crucial, as high-quality labeled data directly impacts the effectiveness of machine learning models and AI applications. Most positions are structured in team environments, where productivity and communication skills help ensure project deadlines are met.

What is the job description of data labeling?

Data labeling involves annotating or tagging data such as images, text, or videos to help machine learning models understand and learn from the data. The role requires attention to detail, familiarity with labeling tools, and adherence to guidelines to ensure high-quality annotations. It is often performed remotely and may involve repetitive tasks with flexible schedules.

How much are data labelers paid?

Data labelers typically earn between $10 and $20 per hour, depending on experience, location, and the complexity of the labeling tasks. Many positions are freelance or remote, with pay rates varying across platforms and employers.

How do I become a data labeler?

To become a data labeler, you typically need basic computer skills, attention to detail, and the ability to follow instructions. Many positions require no formal degree and offer flexible, part-time schedules; familiarity with data annotation tools or platforms is often helpful. Applying through online job boards or company websites is common for entry-level roles.

Is data labelling a good career?

Data labeling is a common entry-level role in data annotation and machine learning workflows, often requiring attention to detail and basic computer skills. It can provide opportunities to develop skills in data management and AI, but typically offers lower pay and limited advancement without additional training or experience.
What are the most commonly searched types of Data Labeling jobs in Michigan? The most popular types of Data Labeling jobs in Michigan are:
What are popular job titles related to Data Labeling jobs in Michigan? For Data Labeling jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Data Labeling jobs in Michigan look for? The top searched job categories for Data Labeling jobs in Michigan are:
What cities in Michigan are hiring for Data Labeling jobs? Cities in Michigan with the most Data Labeling job openings:
Data Labeling jobs near you
Infographic showing various Data Labeling job openings in Michigan as of July 2026, with employment types broken down into 75% Full Time, 17% Part Time, and 8% Contract. Highlights an 100% In-person job distribution.

Senior Robotics Data Engineer - Only W2

Saransh Inc

Warren, MI • On-site

$99K - $135K/yr

Contractor

Re-posted 4 days ago

Job description

Role: Senior Robotics Data Engineer
Location: Warren, MI (Onsite from Day 1)
Job Type: W2 Contract
 
Main Skills: Senior Robotics Data Engineer (ML/AI systems, Python, TensorFlow and/or PyTorch, Power BI, Azure data services)
 
Key Responsibilities:
· Design and implement scalable data pipelines for large-scale robotic datasets (vision, depth, tactile, force/torque).
· Build infrastructure for high-throughput data capture from real robots and simulation environments.
· Develop and deploy semi-supervised/self-supervised data labeling workflows to minimize manual annotation costs.
· Enable simulation-to-real (Sim2Real) data workflows, including domain randomization and synthetic data generation.
· Manage data versioning, metadata, and dataset governance to support model training, evaluation, and regression testing.
· Collaborate with Robotics Perception, Grasping AI, and Simulation teams to define data requirements and KPIs.
· Establish data quality metrics that correlate with perception and grasping performance.
 
Required Qualifications:
· Minimum 3 years of experience in data engineering, machine learning systems, robotics, or related fields.
· Master’s degree in Engineering, Computer Science, Data Science, or equivalent practical experience.
· Proven experience building production-grade data pipelines for ML/AI systems.
· Strong hands-on experience with Python-based data tooling.
· Experience working with large, complex, multimodal datasets.
 
Preferred Qualifications:
· Direct experience supporting robotics perception, grasping, or manipulation 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 of TensorFlow and/or PyTorch from a data systems perspective.
· Experience with Microsoft data ecosystems (Power BI, Azure data services).