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Executive Natural Language Processing Engineer Jobs in New York

... artificial intelligence), NLP (natural language processing), and ML (machine learning ... Experience selling to product and engineering teams. Benefits: * Flexible/unlimited vacation

They are seeking a Senior Data Scientist with extensive experience in machine learning algorithms and strong skills in Text Mining and Natural Language Processing. Responsibilities : • 10 to 15 ...

Machine Learning Engineer II

New York, NY · On-site

$106K - $146K/yr

With expertise in machine learning, natural language processing, and data discovery, we develop and ... We dedicate time and resources to explore new ideas, but always rooted in engineering best ...

Showing results 21-40

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Infographic showing various Executive Natural Language Processing Engineer job openings in New York as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 20% Part Time, 1% Temporary, and 4% Contract. Highlights an 91% Physical, 1% Hybrid, and 8% Remote job distribution.

Machine Learning Engineer / AI Engineer

1 point system

New York, NY • Remote

Contractor

Posted 23 days ago


Job description

Must Haves:
Neural networks
NLP
Python
AZURE
Pytorch or tensorflow
Job Description:
Machine Learning Engineer / AI Engineer Role

Role Overview

This role is focused on developing, deploying, and optimizing machine learning models for enterprise applications. The ideal candidate should have strong hands-on experience with machine learning algorithms, neural networks, NLP, Python/R/SQL, modern ML frameworks, Microsoft Azure, and DevOps/MLOps practices. This is not just a data science research role — the candidate needs to be able to build models and support deployment/management in a production environment.


Must-Have Skills

The candidate must have hands-on experience with:

  • Supervised and/or unsupervised machine learning algorithms
  • Neural networks
  • Natural Language Processing, NLP
  • Python
  • R
  • SQL
  • TensorFlow, Keras, and/or PyTorch
  • Microsoft Azure cloud platform
  • DevOps and/or MLOps practices
  • Model development, deployment, optimization, and lifecycle management