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Ai Data Training Jobs (NOW HIRING)

IntraEdge

AI Data Engineer

Detroit, MI

$113K - $136K/yr

Automate the training and deployment of AI/ML models into production via APIs and microservices. * Monitor and troubleshoot: Implement data observability tools to monitor pipeline health, identify ...

GARGI TECHNOLOGIES INC

AI Data Engineer

Denver, CO · On-site

$117K - $141K/yr

Support LLM training and fine-tuning initiatives. * Optimize data quality for AI applications. * Collaborate with Data Scientists and AI Engineers. Required Skills * Python, SQL * Apache Spark

Burlington

AI Data Scientist

Edgewater Park, NJ

$80K - $105K/yr

Position Overview As a Junior AI Data Scientist, you will support business areas including ... We are a rapidly growing brand and provide a variety of training and development opportunities so ...

Burlington Stores

AI Data Scientist

Beverly, NJ · On-site

Position Overview As a Junior AI Data Scientist, you will support business areas including ... We are a rapidly growing brand and provide a variety of training and development opportunities so ...

ALTEN Technology USA

Practice Manager - AI & Data

Troy, MI · On-site

$160K - $190K/yr

Training and competency development * Provide line management and leadership to members of the practice including technical leadership across AI and Data * Define skills development objectives for ...

ALTEN Technology USA

Practice Manager - AI & Data

Troy, MI · On-site +1

$160K - $190K/yr

Training and competency development * Provide line management and leadership to members of the practice including technical leadership across AI and Data * Define skills development objectives for ...

New

Instant Serve LLC

AI Data Architect /REMOTE

Denver, CO · Remote

$75 - $80/hr

Remote AI Data Architect - Public Sector Location: Preferably Colorado Duration: 6 months We are ... NIST AI Framework training or certification. * Residency in Colorado is preferred. Join us to shape ...

Schonfeld

AI Data Engineer

New York, NY

$125K - $150K/yr

Implement data versioning, lineage tracking, and observability for AI training and inference pipelines. * Optimize data delivery for low-latency AI interactions and high-throughput batch processing.

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Ai Data Training information

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

To thrive as an AI Data Trainer, you need a solid understanding of data annotation, machine learning fundamentals, and attention to detail, often backed by experience in data science or a related field. Familiarity with data labeling tools, annotation platforms, and version control systems is typically required. Strong analytical thinking, communication skills, and the ability to follow complex guidelines set top performers apart in this role. These skills ensure that high-quality, accurate datasets are produced to effectively train and improve AI models.

What is the difference between Ai Data Training vs Data Analyst?

AspectAi Data TrainingData Analyst
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of AI/ML frameworksDegree in Statistics, Mathematics, or related fields; proficiency in data analysis tools
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, healthcare, and other industries
Employer & Industry UsagePrimarily in AI development and machine learning projectsAcross various sectors analyzing data to inform decisions

Ai Data Training involves preparing and labeling data for AI models, focusing on machine learning algorithms. Data Analysts interpret data to generate insights for business decisions. While both roles work with data, Ai Data Training is more technical and model-focused, whereas Data Analysts focus on analysis and reporting.

What is AI data training?

AI data training refers to the process of teaching artificial intelligence systems, such as machine learning models, to recognize patterns and make decisions by feeding them large amounts of labeled data. This involves collecting, annotating, and preprocessing data so that the AI can learn from examples and improve its performance over time. Data trainers play a crucial role in ensuring that the data used is accurate, diverse, and relevant to the AI's intended tasks. Effective AI data training helps models become more accurate, reliable, and capable of handling real-world scenarios.

What are some common challenges faced in AI Data Training roles, and how can they be effectively managed?

Professionals in AI Data Training often encounter challenges such as ensuring data accuracy, managing large and potentially unstructured datasets, and maintaining consistency in labeling. These challenges can be managed through rigorous quality control checks, adopting clear annotation guidelines, and utilizing collaborative tools that streamline the review process. Being detail-oriented and communicating effectively with data scientists and engineers also helps in resolving ambiguities and improving overall data quality.
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Ai Data Training jobs near you
Infographic showing various Ai Data Training job openings in the United States as of June 2026, with employment types broken down into 37% Full Time, 62% Part Time, and 1% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution.

Synthetic Data Engineer (AI Data/Training)

Hyphen Connect Limited

Boston, MA

$124K - $149K/yr

Other

Posted 2 days ago

Job description

We are seeking a talented and innovative Synthetic Data Engineer. In this role, you will design and implement domain-specific synthetic data generation pipelines, ensuring high-quality data management for training loops. Your expertise will drive the success of data processing and model training within the organization.

Responsibilities:

  • Design domain-specific synthetic data generation (SDG) pipelines via self-instruct and constitutional prompting.
  • Implement automated quality scoring and de-duplication systems.
  • Manage data pipelines that feed directly into SFT and DPO training loops.

Qualifications:

  • Proven experience building large-scale data pipelines (Airflow, Spark, Ray).
  • Deep knowledge of prompt engineering for data generation.
  • Familiarity with dataset distillation and bias mitigation.

Apply