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Data Annotation For Ai Jobs in Waterloo, IA (NOW HIRING)

Jobtailor

Data Architect

Cedar Falls, IA · On-site

$140 - $210/hr

Establish patterns for delivering trusted, well‑governed data to AI/ML and analytics use cases. * Evaluate emerging data and AI capabilities and incorporate them into the platform roadmap where ...

Socket.dev

Director of Revenue Operations

Cedar Falls, IA · On-site

$150 - $190/hr

Build and maintain an AI-ready data infrastructure that connects CRM, ERP, product telemetry, and ... Ensure operational readiness for growth -- building processes and systems that scale gracefully ...

DHI Group, Inc.

Software Engineer - Dice

Denver, IA · On-site

... for the people using it. AI-native: * We start with AI. The engineers who thrive here keep asking ... Data modeling and database skills (PostgreSQL or other relational/NoSQL stores): schema design ...

Martin Bros.

IT Product Manager

Cedar Falls, IA · On-site

In a single day you might work through a data model with an engineer, sit with a driver or a ... This is a senior role for someone who has practiced product management at a high level and is ready ...

Sweatpals

Product Manager Internship

Waterloo, IA · On-site

We're an AI-forward company. If you're excited about building real products for real communities at ... Data analysis and synthesis to support roadmap decisions * Where relevant, contribute to 0-to-1 ...

DHI Group, Inc.

Software Engineer - Dice

Denver, IA · On-site

... an impact for thousands of job seekers. We are an AI-native team. We actively use AI coding ... You'll contribute cross-functionally with Product, Design, and Data partners to achieve ongoing ...

Simple Mining LLC

Talent Acquisition Manager

Cedar Falls, IA · On-site

$120K - $160K/yr

Bitcoin and AI. Growth is real, and it is constrained by hiring. How we work We are lean by design ... You will own recruiting for every function and every level -- from data center technicians in rural ...

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All JobsData Annotation For Ai JobsData Annotation For Ai Jobs in Waterloo, IA

Data Annotation For Ai information

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 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.

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 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 job categories do people searching Data Annotation For Ai jobs in Waterloo, IA look for?

The top searched job categories for Data Annotation For Ai jobs in Waterloo, IA are:

What cities near Waterloo, IA are hiring for Data Annotation For Ai jobs?

Cities near Waterloo, IA with the most Data Annotation For Ai job openings:

Data Annotation For Ai jobs near you
Infographic showing various Data Annotation For Ai job openings in Waterloo, IA as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 12% Part Time, and 5% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution.

Data Architect

Jobtailor

Cedar Falls, IA • On-site

$140 - $210/hr

Other

Posted 18 days ago

Job description

Responsibilities
  • Define and own the data architecture strategy across all environments, platforms, and data domains.
  • Establish and maintain architectural standards, patterns, and best practices for data ingestion, storage, modeling, and consumption.
  • Design scalable, secure, and resilient data architectures on AWS, including data lake, lakehouse, warehouse, and serving layers.
  • Define and champion a Medallion (bronze/silver/gold) architecture, establishing standards for raw, refined, and curated data layers and the promotion patterns between them.
  • Lead the platform direction of AgencyBloc's cloud data warehouse/lakehouse, defining how each is used, where workloads run, and how cost and performance are managed.
  • Establish data modeling standards (dimensional, Data Vault, and other patterns) that balance flexibility, performance, and maintainability.
  • Define standards for batch and streaming pipelines, ETL/ELT frameworks, orchestration, and reuse patterns across teams.
  • Lead tool and platform selection decisions (warehouse/lakehouse, ingestion, transformation, orchestration, catalog, BI), ensuring alignment with long‑term strategy.
  • Conduct architecture reviews and provide guidance on complex or high‑impact data initiatives.
  • Collaborate with engineering leadership to align data capabilities with product and business priorities.
  • Define and own the data governance strategy, including data cataloging, lineage, classification, and ownership models.
  • Establish data quality standards and frameworks, including validation, monitoring, and remediation practices.
  • Partner with security and compliance teams to enforce secure‑by‑design principles for sensitive and regulated data, including access controls, encryption, masking, and PII handling (SOC 2 and relevant standards).
  • Define data retention, archival, and lifecycle management standards.
  • Partner with the DevOps Architect to define data pipeline observability standards, including monitoring, alerting, and SLAs/SLOs for freshness, completeness, and reliability, ensuring alerts surface high‑quality, actionable signals tied to business impact with minimal noise.
  • Establish practices for cost monitoring and optimization across data platforms.
  • Drive standardization and reduction of tool and pattern fragmentation across data teams.
  • Mentor data engineers and senior data engineers, providing technical leadership and architectural guidance.
  • Define the organization’s approach to enabling AI and ML workloads on the data platform, including feature data, governance, and adoption strategy.
  • Establish patterns for delivering trusted, well‑governed data to AI/ML and analytics use cases.
  • Evaluate emerging data and AI capabilities and incorporate them into the platform roadmap where they provide measurable value.
Requirements
  • Bachelor’s degree in Computer Science or equivalent experience preferred.
  • 10+ years of experience in data engineering, data architecture, or analytics engineering.
  • Proven experience designing and implementing large‑scale cloud data architectures (AWS preferred).
  • Hands‑on expertise with Databricks and/or Snowflake in a production environment.
  • Demonstrated experience designing and implementing Medallion (bronze/silver/gold) architectures.
  • Strong experience with data modeling (dimensional, Data Vault, and related patterns) for analytical and operational use cases.
  • Deep experience with ETL/ELT design, orchestration, and batch and streaming data pipelines at scale.
  • Strong understanding of data governance, cataloging, lineage, and data quality frameworks.
  • Expertise in data security and compliance, including access management, encryption, and PII handling (SOC 2 and similar frameworks).
  • Strong background in SQL, automation, scripting, and modern data engineering practices (e.g., IaC and CI/CD for data).
  • Experience evaluating and selecting data tools and platforms.
  • Experience leading cross‑team technical initiatives and influencing engineering direction.
  • Strong communication skills, with the ability to translate complex technical concepts to non‑technical stakeholders.
  • Experience working in insurance, InsurTech, or other regulated industries is a plus.
  • Strategic thinking and long‑term planning.
  • Systems design and architectural decision‑making.
  • Cross‑team influence and alignment.
  • Balancing standardization with team autonomy.
  • Pragmatic execution and decision‑making.
Certifications & Qualifications
  • Bachelor’s degree in Computer Science
  • SOC 2 compliance
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