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Natural Language Processing Assistant Professor Jobs

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$72K - $92K/yr

Jobs / Senior AI Research Scientist - Natural Language Processing Senior AI Research Scientist - Natural Language Processing Full-time About the Role Our client, a cutting-edge technology firm at the ...

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Natural Language Processing Assistant Professor information

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$42.5K

$75.4K

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How much do natural language processing assistant professor jobs pay per year?

As of Sep 11, 2026, the average yearly pay for natural language processing assistant professor in the United States is $75,371.00, according to ZipRecruiter salary data. Most workers in this role earn between $68,000.00 and $83,000.00 per year, depending on experience, location, and employer.

What is a natural language processing assistant professor?

A Natural Language Processing (NLP) Assistant Professor is an academic professional who teaches and conducts research in the field of NLP, which involves making computers understand, interpret, and generate human language. They typically work at universities or research institutions, guiding students and contributing to advancements in language technologies such as machine translation, sentiment analysis, and chatbots. Their responsibilities include developing curriculum, publishing research, securing funding, and mentoring students in NLP-related topics.

What are the key skills and qualifications needed to thrive as a natural language processing assistant professor?

To thrive as a Natural Language Processing Assistant Professor, you need a Ph.D. in computer science, computational linguistics, or a related field, along with a strong research background in NLP and machine learning. Proficiency with programming languages (such as Python), NLP frameworks (like NLTK, spaCy, or Hugging Face), and experience publishing in top conferences are typically required. Excellent communication, mentorship, and collaboration skills help in teaching, supervising students, and contributing to academic communities. These skills are essential for advancing research, securing funding, and fostering student success in a rapidly evolving discipline.

What are some common challenges faced by a natural language processing assistant professor in balancing research and teaching responsibilities?

As a Natural Language Processing Assistant Professor, one of the main challenges is effectively balancing time between conducting cutting-edge research and fulfilling teaching obligations. Managing grant applications, mentoring graduate students, preparing course materials, and staying current with rapid advancements in NLP can be demanding. Many professors find it helpful to integrate their research topics into their teaching, fostering a dynamic learning environment while advancing their own work. Collaboration with colleagues and clear time management strategies are essential for success and career progression in this role.

What are popular job titles related to Natural Language Processing Assistant Professor jobs?

For Natural Language Processing Assistant Professor jobs, the most frequently searched job titles are:

Infographic showing various Natural Language Processing Assistant Professor job openings in the United States as of September 2026, with employment types broken down into 2% As Needed, 74% Full Time, 21% Part Time, 1% Temporary, and 2% Contract. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution, with an average salary of $75,371 per year, or $36.2 per hour.

Natural Language Processing Engineer

Washington, DC • On-site

Beyond SOF
Professional, Scientific, and Technical Services • 11 - 50 employees
Learn more about Beyond SOFexternal

Full-time

Re-posted 5 days ago

Job description

Role Summary:
The Natural Language Processing
(NLP) Engineer is responsible for
developing and implementing NLP
solutions to support the
company's projects.
Main Responsibilities and duties:
Develop and implement NLP
solutions.
Collaborate with the engineering
team to integrate NLP solutions
into projects.
Conduct research on NLP
technologies and trends.
Stay updated on the latest NLP
technologies and trends.
Develop and implement
quantum-enhanced NLP
solutions. Collaborate with
quantum engineers to integrate
quantum technologies into NLP
projects.

Apply