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Remote Ai Data Annotator Jobs in Rochester, NY (NOW HIRING)

Innovative Solutions

AI Data Architect

Rochester, NY · Remote

$150K - $200K/yr

As a Data/AI Architect, you'll design and build data-driven cloud architectures on AWS -- from S3 ... This can be a remote opportunity, with 2 weeks of travel into Rochester, NY per quarter What You'll ...

Innovative Solutions

AI Data Architect

Rochester, NY · On-site +1

$150K - $200K/yr

As a Data/AI Architect, you'll design and build data-driven cloud architectures on AWS - from S3 ... This can be a remote opportunity, with 2 weeks of travel into Rochester, NY per quarter What You'll ...

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Remote Ai Data Annotator information

How to become a remote AI data annotator?

To become a remote AI data annotator, you typically need strong attention to detail, basic computer skills, and the ability to follow specific guidelines. Many positions require no formal degree, but familiarity with tools like annotation platforms and some experience with data labeling can be helpful. Applying through online job boards and demonstrating accuracy and reliability are key steps in securing such roles.

What is the difference between Remote Ai Data Annotator vs Remote Machine Learning Data Labeler?

AspectRemote Ai Data AnnotatorRemote Machine Learning Data Labeler
Required CredentialsBasic computer skills, training in annotation toolsSimilar, often no formal degree required
Work EnvironmentRemote, flexible hours, task-basedRemote, task-based, often part-time
Industry UsageAI, machine learning, autonomous vehicles, healthcareAI, machine learning, computer vision, NLP
Search & Comparison IntentHigh overlap, often compared for entry-level rolesSimilar roles, slightly more specialized in data types

The Remote Ai Data Annotator and Remote Machine Learning Data Labeler roles are quite similar, often requiring basic technical skills and working remotely in AI-related industries. The main difference lies in terminology and specific data types labeled, but both roles serve as entry points into AI data preparation, with overlapping skills and work environments.

What skills and qualifications are needed to thrive as a remote AI data annotator?

To thrive as a Remote AI Data Annotator, you need keen attention to detail, strong analytical skills, and familiarity with data labeling concepts, often supported by a high school diploma or higher. Experience with annotation platforms, data management tools, and sometimes knowledge of scripting languages like Python is valuable. Strong communication, time management, and self-motivation are essential soft skills for meeting deadlines and working independently. These skills ensure high-quality, consistent data annotation, which is crucial for developing accurate AI and machine learning models.

What are common challenges faced by remote AI data annotators, and how can they be addressed?

Remote AI data annotators often encounter challenges such as maintaining focus during repetitive tasks, ensuring consistent labeling accuracy, and managing effective communication with team members across different time zones. To address these, it's helpful to establish a structured work schedule, use productivity tools to minimize distractions, and actively participate in team meetings or chats to clarify annotation guidelines. Regular feedback sessions and quality checks also play a key role in maintaining high annotation standards and fostering professional growth within the team.

What is a remote AI data annotator?

Remote AI Data Annotators are professionals who label, categorize, or tag data such as images, text, audio, or video to train and improve artificial intelligence (AI) models. They work remotely, often from home, using specialized software to ensure data is accurate and useful for machine learning algorithms. Their work is essential for helping AI systems learn to recognize patterns, make predictions, and perform tasks like image recognition or natural language processing. Annotators must be detail-oriented and follow specific guidelines to ensure the quality and consistency of labeled data.
What are popular job titles related to Remote Ai Data Annotator jobs in Rochester, NY? For Remote Ai Data Annotator jobs in Rochester, NY, the most frequently searched job titles are:
What job categories do people searching Remote Ai Data Annotator jobs in Rochester, NY look for? The top searched job categories for Remote Ai Data Annotator jobs in Rochester, NY are:
What cities near Rochester, NY are hiring for Remote Ai Data Annotator jobs? Cities near Rochester, NY with the most Remote Ai Data Annotator job openings:
Remote Ai Data Annotator jobs near you
Infographic showing various Remote Ai Data Annotator job openings in Rochester, NY as of August 2026, with employment types broken down into 100% Part Time. Highlights an 100% Remote job distribution.

AI Data Architect

Innovative Solutions

Rochester, NY • Remote

$150K - $200K/yr

Full-time

Re-posted 5 days ago

Job description

As a Data/AI Architect, you'll design and build data-driven cloud architectures on AWS — from S3 data lakes and Glue ETL pipelines to data warehouses and RAG-powered AI systems. You'll own the full data stack across a variety of industries and projects: one engagement you're designing a Redshift data warehouse with medallion architecture processing 31M transactions/month, the next you're building a Bedrock Knowledge Base with OpenSearch vector search. Real ownership, real variety.

Location: This can be a remote opportunity, with 2 weeks of travel into Rochester, NY per quarter

What You'll Do:

  • Design and build S3 data lakes with multi-zone organization, partitioning strategies, lifecycle policies, and encryption
  • Implement medallion architecture (bronze/silver/gold) for data warehouses on Redshift, Snowflake, or Databricks
  • Build AWS Glue ETL pipelines (Python Shell and Spark) with incremental extraction, Data Catalog management, and optimized Parquet output
  • Design star/snowflake schemas, materialized views, and gold-layer models optimized for BI consumption (QuickSight, PowerBI)
  • Configure data warehouse platforms — Redshift with Zero-ETL from Aurora, Snowflake with Snowpipe, Databricks with Delta Lake and Auto Loader
  • Design RAG systems using Bedrock Knowledge Base with OpenSearch Serverless vector search and Titan Embeddings
  • Architect document AI pipelines using Textract, Comprehend, and Bedrock for entity extraction
  • Design SageMaker ML pipelines for training, Model Registry, and inference
  • Lead data discovery sessions with client stakeholders and present architecture recommendations to technical and business audiences
  • Mentor delivery team members on data architecture patterns and AWS data services
  • Contribute to R&D projects evaluating emerging AWS data and AI capabilities
 

Required Skills:

  • 5+ years professional IT experience, 2+ years professional AWS experience
  • At least one AWS Professional-level certification (Solutions Architect Professional or Data Engineer Specialty preferred)
  • Python for data pipelines (Glue jobs, Lambda, SageMaker scripts) and PySpark for Glue Spark jobs
  • SQL and NoSQL on AWS — Aurora PostgreSQL, RDS PostgreSQL, DocumentDB, DynamoDB — including schema design and query optimization
  • Data modeling — conceptual, logical, and physical models for AWS data platforms; normalized silver-layer schemas, denormalized star/snowflake gold-layer schemas, data dictionaries
  • Dimensional modeling and medallion architecture (bronze/silver/gold) on Redshift, Snowflake, or Databricks, including materialized views and incremental refresh patterns
  • AWS Glue ETL (Python Shell and Spark), Glue Data Catalog, and crawlers
  • S3 data lake architecture with partitioning, lifecycle policies, and encryptions
 

Preferred:

  • RAG systems with Bedrock Knowledge Base and OpenSearch Serverless vector search
  • Amazon SageMaker for ML training, Model Registry, and inference
  • AWS HealthLake, FHIR R4 transformation, and HIPAA-compliant data pipelines
  • Document AI with Amazon Textract and Comprehend
  • Amazon Athena, QuickSight, or PowerBI integration
  • Terraform or CloudFormation for data infrastructure as code
  • Step Functions, EventBridge, and Lambda for event-driven pipeline orchestration

The salary range provided is a general guideline. When extending an offer, Innovative considers factors including, but not limited to, the responsibilities of the specific role, market conditions, geographic location, as well as the candidate’s professional experience, key skills, and education/training.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.