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Entry Level Ai Data Labeling 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 ...

Accenture

Data Protection Sr. Analyst

Detroit, MI · Hybrid

$84K - $100K/yr

... labels/encryption, DLP policies, data classification/discovery, Insider Risk Management ... Contribute to emerging areas such as AI and agentic AI security, under guidance. . Qualifications ...

Pwc

AI & GenAI Data Scientist - Manager

Detroit, MI

$99K - $232K/yr

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Manager & Summary ... PwC does not intend to hire experienced or entry level job seekers who will need, now or in the ...

Pwc

AI & GenAI Data Scientist - Manager

Grand Rapids, MI

$99K - $232K/yr

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Manager & Summary ... PwC does not intend to hire experienced or entry level job seekers who will need, now or in the ...

SynergisticIT

Entry-Level Data Engineer

Detroit, MI · On-site

$104K - $125K/yr

... for entry-level software programmers, Java Full stack developers, Python/Java developers, Data ... Snowflake, Databricks, LLM, Gen AI, Text mining, Tableau, PowerBI, SAS, Tensorflow If you get ...

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Entry Level Ai Data Labeling information

What are some common challenges faced by entry-level AI data labelers, and how can they be addressed?

Entry-level AI data labelers often encounter challenges such as repetitive tasks, maintaining high accuracy under tight deadlines, and understanding complex labeling guidelines. To address these, it's important to take regular breaks to avoid fatigue, seek clarification from team leads when instructions are unclear, and leverage training resources provided by the company. Collaborating with peers and utilizing feedback can also improve efficiency and accuracy, making the role both manageable and rewarding for those starting their careers in AI.

What is the difference between Entry Level Ai Data Labeling vs Data Annotation Specialist?

AspectEntry Level Ai Data LabelingData Annotation Specialist
CredentialsBasic computer skills, no formal certification often requiredSimilar; often no formal certification, but some roles prefer training in data management
Work EnvironmentRemote or on-site, flexible hours, task-basedRemote or on-site, similar flexible environment
Industry UsageCommon in AI/ML companies, tech startupsUsed across tech, healthcare, automotive industries
Search/Comparison IntentHigh overlap, both involve labeling data for AI

Entry Level Ai Data Labeling and Data Annotation Specialist roles both involve labeling data to train AI models. While they share similar credentials and work environments, the term "Data Annotation Specialist" is often used interchangeably but may imply a broader scope or more specialized tasks. Both roles are essential in AI development and typically require minimal formal education, focusing on accuracy and attention to detail.

What is entry level AI data labeling?

Entry level AI data labeling involves tagging, categorizing, or annotating data such as images, text, audio, or video to help train artificial intelligence models. Data labelers follow specific guidelines to ensure the data is accurately marked, which is essential for machine learning algorithms to learn and make predictions. This role usually requires attention to detail, basic computer skills, and the ability to follow instructions, but typically does not require advanced technical experience. Data labeling is foundational to the development of reliable AI systems.

What are the key skills and qualifications needed to thrive as an Entry Level AI Data Labeler, and why are they important?

To thrive as an Entry Level AI Data Labeler, you need strong attention to detail, basic computer literacy, and the ability to follow specific instructions, typically with at least a high school diploma or equivalent. Familiarity with annotation tools, data labeling platforms, and sometimes spreadsheet software is commonly required. Reliability, focus, and effective communication are important soft skills that help ensure high-quality, consistent work. These skills and qualities are vital for producing accurate datasets that directly impact the performance and reliability of AI models.
What are the most commonly searched types of Ai Data Labeling jobs in Michigan? The most popular types of Ai Data Labeling jobs in Michigan are:
What are popular job titles related to Entry Level Ai Data Labeling jobs in Michigan? For Entry Level Ai Data Labeling jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Entry Level Ai Data Labeling jobs in Michigan look for? The top searched job categories for Entry Level Ai Data Labeling jobs in Michigan are:
What cities in Michigan are hiring for Entry Level Ai Data Labeling jobs? Cities in Michigan with the most Entry Level Ai Data Labeling job openings:
Entry Level Ai Data Labeling jobs near you

Senior Robotics Data Engineer - Only W2

Saransh Inc

Warren, MI • On-site

$99K - $135K/yr

Contractor

Posted yesterday

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).