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Machine Learning Natural Language Processing Jobs

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As a Machine Learning at BetterHelp, you'll join a diverse team of licensed clinicians, engineers ... This role will focus on Natural Language Processing (NLP), Large Language Models (LLMs), evaluation ...

BetterHelp

Machine Learning Engineer

As a Machine Learning at BetterHelp, you'll join a diverse team of licensed clinicians, engineers ... This role will focus on Natural Language Processing (NLP), Large Language Models (LLMs), evaluation ...

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How much do machine learning natural language processing jobs pay per hour?

As of Jul 14, 2026, the average hourly pay for machine learning natural language processing in the United States is $19.89, according to ZipRecruiter salary data. Most workers in this role earn between $17.79 and $21.63 per hour, depending on experience, location, and employer.

How do professionals in Machine Learning Natural Language Processing typically collaborate with cross-functional teams?

In Machine Learning Natural Language Processing (NLP) roles, collaboration with cross-functional teams is essential. NLP professionals frequently work alongside data engineers to gather and preprocess large datasets, software developers to integrate models into products, and product managers to align technical solutions with business goals. Regular communication, agile methodologies, and collaborative problem-solving sessions are common to ensure that NLP models are both technically sound and user-oriented. This teamwork not only enhances the effectiveness of NLP solutions but also provides opportunities to learn from colleagues with diverse expertise.

What is a Machine Learning Natural Language Processing (NLP) specialist?

A Machine Learning Natural Language Processing (NLP) specialist is a professional who designs, develops, and implements algorithms and models that allow computers to understand, interpret, and generate human language. They use machine learning techniques to work on tasks such as text classification, sentiment analysis, language translation, and speech recognition. NLP specialists often work with large datasets and require strong skills in programming, linguistics, and data science. Their work enables applications like chatbots, virtual assistants, and search engines to better process human language.

What are the key skills and qualifications needed to thrive as a Machine Learning Natural Language Processing specialist, and why are they important?

A Machine Learning Natural Language Processing (NLP) specialist needs strong programming skills (especially in Python), a solid understanding of machine learning algorithms, and advanced knowledge in linguistics or computational linguistics, often supported by a relevant degree. Familiarity with NLP libraries (like NLTK, spaCy, or Hugging Face Transformers), experience with machine learning frameworks (such as TensorFlow or PyTorch), and sometimes certifications in data science or AI are typically required. Excellent problem-solving ability, collaboration, and effective communication are crucial soft skills to excel in this field. These skills enable the development of robust NLP models, smooth integration into real-world applications, and effective teamwork on complex projects.
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Machine Learning Natural Language Processing jobs near you

Machine Learning Scientist - Natural Language Processing (NLP) - Senior Associate - Machine Learning

JPMorganChase

Manhattan, NY • On-site

$65K - $65K/yr

Full-time

Posted 14 days ago

Job description

Job Summary:
JPMorgan Chase is a leading global financial institution that invests heavily in innovation and technology. They are seeking a Machine Learning Scientist – Natural Language Processing (NLP) - Senior Associate to develop and deploy machine learning solutions, particularly in Generative AI, while collaborating across various business units to drive transformational change.
Responsibilities:
• Research and develop state-of-the-art machine learning models to solve real-world problems and apply them to tasks involving Generative AI (GenAI)
• Act as a thought partner for JPMC leaders and help the business identify and implement new machine learning methods that deliver impact
• Drive cross-functional collaboration with multiple partner teams such as Business, Technology, Product Management, Legal, Compliance, Strategy, and Business Management to deploy solutions into production
• Lead firm-wide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business
Qualifications:
Required:
• PhD in a quantitative discipline, e.g., Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science, OR an MS with at least 2 years of industry or research experience in the field
• Solid background in Generative AI (GenAI) and hands-on experience and solid understanding of machine learning and deep learning methods and toolkits (e.g., TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
• Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
• Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments
• Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences
Preferred:
• Strong background in Mathematics and Statistics; Familiarity with the financial services industries and continuous integration models and unit test development
• Knowledge in search/ranking or Meta Learning
• Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large-scale distributed environment, and ability to develop and debug production-quality code
• Published research in areas of Machine Learning or Deep Learning at a major conference or journal
Company:
With a history tracing its roots to 1799 in New York City, JPMorganChase is one of the world's oldest, largest, and best-known financial institutions—carrying forth the innovative spirit of our heritage firms in global operations across 100 markets. Founded in 2000, the company is headquartered in New York, USA, with a team of 10001+ employees. The company is currently Late Stage.

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