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Nlp Jobs (NOW HIRING)

United IT Solutions

GenAI NLP ML Engineer

Austin, TX

GenAI NLP ML Engineer Location: Austin TX Key Responsibilities : * 10+ yrs experience minimum * Collaborate and manage with data science , engineering , and GenAI teams to deploy and scale machine ...

Machinify

Staff Data Scientist | NLP

$180K - $230K/yr

Design and train NLP models for tasks like classification, entity extraction, retrieval, summarization, and semantic search * Fine-tune and evaluate LLMs (open-source and API-based); build RAG ...

United IT

GenAI NLP ML Engineer

Austin, TX

GenAI NLP ML Engineer Location: Austin TX Key Responsibilities: * 10+ yrs experience minimum * Collaborate and manage with data science, engineering, and GenAI teams to deploy and scale machine ...

NLP Logix

Marketing Coordinator

Jacksonville, FL · On-site

$60K - $80K/yr

About the Role NLP Logix is seeking a motivated and detail-oriented Marketing Coordinator to support lead generation, digital marketing, social media, content, and event-related marketing activities.

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How much do nlp jobs pay per year?

As of Jun 9, 2026, the average yearly pay for nlp in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What is an NLP job?

An NLP (Natural Language Processing) job involves developing algorithms and models that enable computers to understand, interpret, and generate human language. NLP professionals work on tasks such as text analysis, speech recognition, machine translation, and chatbot development. They typically use machine learning, deep learning, and linguistic techniques to improve natural language comprehension in applications like virtual assistants, search engines, and sentiment analysis tools.

What are the typical projects or tasks that an NLP specialist works on within a company?

NLP specialists commonly work on projects such as building chatbots, developing text classification models, automating document analysis, or improving search engines and recommendation systems. Daily tasks can include annotating data, training and evaluating models, collaborating with data engineers and product teams, and staying up-to-date with the latest research. You'll often be involved in both the development and deployment of language-based AI solutions, which might include integrating APIs, optimizing performance, or addressing real-world language challenges. Collaboration and communication are key, as NLP projects typically involve cross-functional teams spanning engineering, data science, and business stakeholders.

What are the key skills and qualifications needed to thrive in the Nlp position, and why are they important?

To thrive in Natural Language Processing (NLP), you need a solid background in computer science, linguistics, and mathematics, often supported by a relevant degree or advanced studies. Familiarity with machine learning frameworks (such as TensorFlow or PyTorch), programming languages like Python, and experience with NLP libraries (e.g., spaCy, NLTK) are crucial, and certifications in AI or data science can be beneficial. Strong analytical thinking, attention to detail, and the ability to communicate technical concepts clearly are important soft skills. These skills are essential for designing, developing, and optimizing language-based AI solutions that address complex linguistic challenges in real-world applications.

What jobs are there in NLP?

Jobs in NLP include roles such as NLP Engineer, Data Scientist, Machine Learning Engineer, and Research Scientist, focusing on developing algorithms for language understanding, speech recognition, and text analysis. These positions often require skills in programming languages like Python, knowledge of NLP libraries, and experience with machine learning frameworks. They are found in tech companies, research institutions, and industries like healthcare, finance, and customer service.
More about Nlp jobs
What cities are hiring for Nlp jobs? Cities with the most Nlp job openings:
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What states have the most Nlp jobs? States with the most job openings for Nlp jobs include:
What job categories do people searching Nlp jobs look for? The top searched job categories for Nlp jobs are:
Nlp jobs near you
Infographic showing various Nlp job openings in the United States as of June 2026, with employment types broken down into 1% Internship, 95% Full Time, 1% Part Time, and 3% Contract. Highlights an 80% Physical, 4% Hybrid, and 16% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

GenAI NLP ML Engineer

United IT Solutions

Austin, TX

Other

Posted 23 days ago

Job description

Role: GenAI NLP ML Engineer
Location: Austin TX
Key Responsibilities :
  • 10+ yrs experience minimum
  • Collaborate and manage with data science, engineering, and GenAI teams to deploy and scale machine learning and generative AI models.
  • Operationalize complex ML and GenAI models into production environments, ensuring end-to-end deployment and monitoring.
  • Apply knowledge of standard ML algorithms (Regression, Classification), NLP concepts (sentiment analysis, topic modeling, TF-IDF), and Generative AI techniques (LLMs, prompt engineering, embeddings).
  • Apply knowledge of Retrieval Augmented Generation using embedding models and Vector databases.
  • Manage delivery of GenAI/LLM features (prompt engineering, evaluation metrics, retrieval patterns, guardrails) and productionizing Q&A/assistant workflows.
  • Lead Platform and DevOps: CI/CD, containerization, observability, and environment automation in a major cloud - ideally working experience on Google.
  • Utilize Python and ML/GenAI libraries such as scikit-learn, PySpark, pandas and Hugging Face Transformers for model development and optimization.
  • Design, develop, and maintain adaptable data pipelines tailored to use-case-specific requirements.
  • Integrate ML and GenAI use cases into business workflows, ensuring seamless data exchange with upstream and downstream systems.
  • Build and maintain pipelines for model performance metrics, supporting Model Risk Oversight and compliance review cadences.
  • Develop runbooks and provide ongoing support for operationalized models to ensure reliability and scalability.

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