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Remote Nlp Jobs (NOW HIRING)

Apex Informatics

AI ML Engineer

Optum Direct client 100% remote EST or CST Profiles only USC/ GC Pay rate Negotiable, be in the range of 60 w2 AI/ML engineer onsite with NLP and text architecture experience. With pyspark, PyTorch ...

YO IT Consulting

Rust Developer - Remote

... high-impact, remote role. The successful candidate will design, implement, and improve robust ... processing (NLP), computer vision, and related technologies -- to solve real-world problems ...

YO IT Consulting

English Language Expert - Remote

In this remote contractor role, you will review AI-generated English-language responses, generate ... processing (NLP), computer vision, and related technologies -- to solve real-world problems ...

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How much do remote nlp jobs pay per year?

As of Jun 29, 2026, the average yearly pay for remote nlp in the United States is $127,031.00, according to ZipRecruiter salary data. Most workers in this role earn between $109,000.00 and $143,500.00 per year, depending on experience, location, and employer.

What types of projects or daily tasks are typical for a Remote NLP professional?

As a Remote NLP professional, your daily work often involves developing, testing, and deploying models that analyze or generate human language, such as chatbots, text classifiers, sentiment analyzers, or recommendation systems. You might spend time cleaning and preprocessing large datasets, collaborating with data scientists or engineers on model optimization, and communicating results to non-technical stakeholders. Remote NLP roles also require strong documentation and regular virtual check-ins to stay aligned with project goals. This environment offers the flexibility of remote work while engaging with innovative projects at the intersection of AI and language.

What is a Remote NLP job?

A Remote NLP (Natural Language Processing) job involves working on language-related AI tasks such as text analysis, machine learning models, and chatbot development from a remote location. These roles typically require expertise in programming languages like Python, NLP libraries (e.g., spaCy, NLTK, or Transformers), and machine learning techniques. Remote NLP professionals collaborate with teams online to develop language models, improve search algorithms, and enhance automated text processing systems.

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

To thrive as a Remote NLP (Natural Language Processing) professional, you need strong programming skills (often in Python), a solid understanding of machine learning and linguistics, and typically a degree in computer science or a related field. Experience with NLP libraries such as NLTK, spaCy, or Hugging Face Transformers, and familiarity with cloud-based development platforms are highly beneficial. Outstanding communication, problem solving, and self-motivation are crucial for effective collaboration in a remote environment. These combined technical and soft skills are essential for building robust language-based AI solutions while staying productive and engaged as part of a distributed team.

More about Remote Nlp jobs
What cities are hiring for Remote Nlp jobs? Cities with the most Remote Nlp job openings:
What are the most commonly searched types of Nlp jobs? The most popular types of Nlp jobs are:
What states have the most Remote Nlp jobs? States with the most job openings for Remote Nlp jobs include:
What job categories do people searching Remote Nlp jobs look for? The top searched job categories for Remote Nlp jobs are:
Remote Nlp jobs near you
Infographic showing various Remote Nlp job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution, with an average salary of $127,031 per year, or $61.1 per hour.
Senior AI Engineer / Data Scientist

Senior AI Engineer / Data Scientist

Koantek

Chandler, AZ • Remote

Full-time

Posted 7 days ago

Job description

Senior AI Engineer / Data Scientist (Consulting)
Location: United States (Remote)
Employment Type: Full-Time / Contract
Experience Level: Senior
About the Role:
We are seeking an experienced, highly technical Senior AI Engineer / Data Scientist to join our customer-facing consulting team. This remote role requires a unique blend of advanced Machine Learning (ML) expertise, deep knowledge of MLOps principles, and a proven track record in client-facing implementation.
You will design, deploy, and maintain production-grade ML solutions, including advanced Generative AI and NLP models, for our diverse client base.
Key Responsibilities:
* Technical Consulting: Lead end-to-end ML implementations directly with clients, translating business problems into robust technical solutions.
* MLOps and Pipelines: Design, build, and maintain production-grade ML pipelines with a strong focus on CI/CD, automation, and scalability.
* GenAI and NLP Deployment: Implement and optimize cutting-edge Generative AI applications (such as LLMs and RAG) in live production settings.
* Infrastructure and Data Scale: Manage underlying infrastructure using Docker, pipeline orchestrators, and distributed computing frameworks like Apache Spark.
* Stakeholder Management: Clearly communicate technical findings, proposals, and project status to both technical and non-technical audiences.
Required Qualifications:
* 4+ years of professional experience developing, deploying, and maintaining ML models in a live production environment (Mandatory).
* 3+ years of experience in a customer-facing consulting or Solutions Architect role.
* Strong expertise in the MLOps lifecycle (model versioning, testing, monitoring, and automated deployment).
* Solid hands-on experience with containerization (Docker) and data pipeline orchestration.
* Proven track record of deploying Generative AI and NLP solutions for client applications.
* Excellent verbal and written communication skills.
Preferred Qualifications:
* Hands-on experience with modern ML platform stacks, specifically Databricks MLOps Stacks.
* Deep knowledge of large-scale data processing and distributed machine learning techniques.
* A strong commitment to continuous learning in emerging ML fields and GenAI application architectures.

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