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Remote Ai Data Collection Jobs in Michigan (NOW HIRING)

Utilidata

Principal Data Engineer

Ann Arbor, MI · On-site +1

$170K - $210K/yr

Utilidata is a fast-growing NVIDIA-backed AI company enabling AI data centers to dynamically ... Experience with data collection from edge devices * Experience supporting ML workflows, including ...

Utilidata

AI Infrastructure Engineer

Ann Arbor, MI · On-site +1

$170K - $210K/yr

... data science teams and is open to fully remote candidates, with periodic travel expected for ... Lead the design and build of Utilidata\'s AI inference platform -- establishing architecture ...

Utilidata

AI Infrastructure Engineer

Ann Arbor, MI · On-site +1

$170K - $210K/yr

... data science teams and is open to fully remote candidates, with periodic travel expected for ... Lead the design and build of Utilidata's AI inference platform - establishing architecture patterns ...

Vervint

Director of Data Intelligence

Grand Rapids, MI · On-site +1

Director of Data Intelligence | Remote | Michigan or Minnesota Preferred Role Snapshot: * Set the Vision: Own the strategy and execution of the Data, Analytics and AI Practice. * Build the Practice:

Deloitte

GCP Manager

Detroit, MI · On-site +1

... or remote client service delivery. Recruiting for this role ends on 06/30/2026. Work you'll do As a GCP Manager on the AI & Data team, you will be responsible for... * Drive solution reviews and ...

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Remote Ai Data Collection information

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

To thrive as a Remote AI Data Collection Specialist, you need attention to detail, data management skills, and a basic understanding of machine learning concepts, often supported by a degree in computer science or related fields. Familiarity with data annotation tools, spreadsheets, and platforms like Labelbox or Amazon SageMaker is commonly required. Strong communication, time management, and problem-solving skills are important for collaborating remotely and meeting project deadlines. These abilities ensure accurate, efficient data gathering and annotation, which are critical for the quality and reliability of AI model development.

What is remote AI data collection?

Remote AI data collection refers to the process of gathering and labeling data—such as images, audio, text, or video—from various sources using digital tools, often from a remote location. This data is used to train and improve artificial intelligence and machine learning models. People working in this field can perform tasks like annotating images, transcribing audio, or categorizing text, all from their home or another remote setting. The work is essential for creating accurate AI systems and often offers flexible hours. It usually requires basic computer skills and attention to detail.

What is the difference between Remote Ai Data Collection vs Remote Data Annotator?

AspectRemote Ai Data CollectionRemote Data Annotator
Required CredentialsBasic computer skills, training in data collection toolsAttention to detail, familiarity with annotation software
Work EnvironmentRemote, flexible hours, often on mobile or desktopRemote, flexible hours, often on desktop or specialized platforms
Industry UsageAI training data gathering across various sectorsLabeling and annotating data for machine learning models
Common Search IntentJobs involving data collection for AIJobs focused on data labeling and annotation

Remote Ai Data Collection involves gathering raw data for AI training, often requiring basic technical skills. Remote Data Annotator focuses on labeling and annotating data to improve machine learning models. Both roles are remote, but they differ in tasks and skill requirements, serving different stages of AI data preparation.

What are some common challenges faced in a Remote AI Data Collection role, and how can they be managed?

A common challenge in Remote AI Data Collection roles is ensuring data quality and consistency, especially when working independently without direct supervision. It is important to follow detailed guidelines precisely and communicate proactively with project managers or team leads whenever uncertainties arise. Time management and maintaining motivation can also be challenging when working remotely, so setting a structured schedule and leveraging collaboration tools can help. Regular check-ins with the team and staying updated with project requirements are key to overcoming these challenges and delivering reliable results.
What are the most commonly searched types of Ai Data Collection jobs in Michigan? The most popular types of Ai Data Collection jobs in Michigan are:
What are popular job titles related to Remote Ai Data Collection jobs in Michigan? For Remote Ai Data Collection jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Remote Ai Data Collection jobs in Michigan look for? The top searched job categories for Remote Ai Data Collection jobs in Michigan are:
What cities in Michigan are hiring for Remote Ai Data Collection jobs? Cities in Michigan with the most Remote Ai Data Collection job openings:
Remote Ai Data Collection jobs near you
AI Data Scientist

AI Data Scientist

Strategic Staffing Solutions

Detroit, MI • On-site, Remote

Other

Posted 9 days ago

Job description

Job Description STRATEGIC STAFFING SOLUTIONS (S3) HAS AN OPENING. AI Data Scientist Remote in EST/CST W2 contract role 6 Months then eligible for Contract renewal Role Overview The Advanced Analytics Hub team is looking to bring on board an Expert AI Data Scientist for an AI project as a Contractor. The objective of this project is to build an intelligent, agentic AI solution that provides material recommendations from SRM Material Catalogs at the point of purchase requisition, leveraging RAG and LLM-based capabilities.

Key Requirements: End-to-End Agentic RAG System Design - Proven experience designing and deploying production-grade RAG systems, including embeddings, vector search, and agent orchestration for multi-step reasoning workflows. LLM & GenAI Integration at Scale (with Agent Frameworks) - Hands-on expertise integrating LLMs into enterprise applications, including prompt engineering, tool usage, and experience with frameworks such as LangGraph/LangChain, Semantic Kernel, or AutoGen. Retrieval Quality, Evaluation & Optimization - Strong background in evaluation frameworks (precision/recall, grounding accuracy, hallucination detection) and optimization techniques (chunking, re-ranking, hybrid search).

MLOps & Productionalization - Experience deploying AI solutions at scale with CI/CD pipelines, model lifecycle management, monitoring, and cloud environments (Azure preferred). Strong ML & Statistical Foundation - Deep expertise in Python, ML/statistics, and experimentation with a focus on rigorous validation of model performance and business impact. Systems Thinking & Enterprise Integration - Ability to architect and integrate AI solutions within enterprise ecosystems (preferably SAP/SRM or similar procurement workflows).

Vector Databases & Retrieval Infrastructure - Hands-on experience with vector databases (e.g., Azure AI Search, Pinecone, FAISS) and optimization for real-time use cases. Core Engineering Best Practices - Strong proficiency in Python, Git, API development, and modern software engineering practices including CI/CD for ML systems. Experience running local models - we run local models on high compute servers on prem (500 GB RAM, 4 L40s GPUs, 64 cpu running on RHEL 9.7) before deploying the solution on cloud platform to save us the cloud cost during development *Beware of scams

S3 never asks for money during its onboarding process

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