ZipRecruiter

Log In

1

Nlp Practitioner Jobs (NOW HIRING)

Centific

Technical Intern

$40/hr

... 000 AI practitioners and engineers. We harness the power of an integrated solution ecosystem ... Do you want to delve into the latest industry advancements in NLP, Computer Vision, Machine ...

6sense

Sr. Data Scientist

San Francisco, CA · Remote

$162K - $238K/yr

... practitioner who enjoys building production-grade AI systems and transforming cutting-edge research into impactful customer-facing products. You will work on advanced machine learning, NLP, LLMs ...

Trunk Tools

Technical Intern

$40/hr

... 000 AI practitioners and engineers. We harness the power of an integrated solution ecosystem ... Do you want to delve into the latestindustryadvancements in NLP, Computer Vision, Machine Learning ...

Cacheflow

Sr. Data Scientist

San Francisco, CA · On-site

$162.92 - $238.95/hr

... practitioner who enjoys building production‑grade AI systems and transforming cutting‑edge research into impactful customer‑facing products. You will work on advanced machine learning, NLP ...

next page

Showing results 1-20

All JobsGovernment JobsGovernment Services JobsLanguage Interpreter JobsNlp Practitioner Jobs

Nlp Practitioner information

See salary details

$41.5K

$130.3K

$200K

How much do nlp practitioner jobs pay per year?

As of Aug 21, 2026, the average yearly pay for nlp practitioner in the United States is $130,295.00, according to ZipRecruiter salary data. Most workers in this role earn between $108,000.00 and $150,000.00 per year, depending on experience, location, and employer.

What is an NLP practitioner?

An NLP (Neuro-Linguistic Programming) Practitioner is a professional who uses NLP techniques to help individuals improve their mindset, communication, and personal development. They apply psychological strategies to reprogram limiting beliefs, enhance confidence, and facilitate behavioral change. NLP Practitioners often work as coaches, therapists, or consultants, assisting clients in achieving specific goals in areas like business, relationships, and personal growth.

What are the key skills and qualifications needed to thrive as an NLP practitioner?

To thrive as an NLP Practitioner, you need a strong understanding of neurolinguistic programming techniques, excellent interpersonal skills, and a relevant certification or formal training in NLP. Familiarity with tools such as coaching frameworks, assessment instruments, and accredited NLP training programs is also important. Strong active listening, empathy, and effective communication skills set outstanding practitioners apart in their ability to build rapport and foster positive client outcomes. These abilities are crucial for successfully guiding clients through personal development and behavioral change processes.

What are common challenges faced by NLP practitioners in their daily work?

One common challenge NLP Practitioners face is tailoring techniques to meet each client’s unique needs, as individuals respond differently to various interventions. Practitioners may also encounter clients who are initially skeptical or resistant, requiring patience and adaptive communication strategies. Balancing multiple clients, maintaining confidentiality, and staying updated with continuing education can also be demanding aspects of the role. However, overcoming these challenges is rewarding, as it leads to meaningful client progress and personal fulfillment.

Are NLP Practitioner certifications worth it?

NLP Practitioner certifications can enhance credibility and demonstrate foundational knowledge of neuro-linguistic programming techniques, which may benefit professionals in coaching, therapy, or personal development roles. However, their value depends on industry recognition and individual career goals, as practical experience and skills often weigh more in hiring decisions.

How much do NLP practitioners make?

NLP practitioners typically earn between $70,000 and $120,000 annually, depending on experience, location, and industry. Entry-level roles may start lower, while experienced professionals with specialized skills in machine learning and programming can earn higher salaries.

What jobs can you get with NLP certification?

NLP practitioners can pursue roles such as natural language processing engineer, data scientist, machine learning engineer, or AI researcher. These jobs typically require skills in programming, data analysis, and familiarity with NLP tools like Python, TensorFlow, or spaCy.
More about Nlp Practitioner jobs

What cities are hiring for Nlp Practitioner jobs?

Cities with the most Nlp Practitioner job openings:

What are the most commonly searched types of Nlp Practitioner jobs?

The most popular types of Nlp Practitioner jobs are:

What states have the most Nlp Practitioner jobs?

States with the most job openings for Nlp Practitioner jobs include:

What job categories do people searching Nlp Practitioner jobs look for?

The top searched job categories for Nlp Practitioner jobs are:

Nlp Practitioner jobs near you
Infographic showing various Nlp Practitioner job openings in the United States as of August 2026, with employment types broken down into 3% As Needed, 82% Full Time, 12% Part Time, and 3% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $130,295 per year, or $62.6 per hour.

GenAI Engineer - Remote

MM International

San Francisco, CA • Remote

Contractor

Re-posted 4 days ago

Job description

GenAI/LLM Engineer (NLP, TensorFlow, PyTorch SME)

San Francisco, Bay Area, CA

Duration: Six months may extend to 12 months

Must be in the Greater Bay area – or in California

Domain: utilities

GenAI/LLM Engineer (NLP, TensorFlow, PyTorch SME)

Implementing GenAI requires specialized expertise in large language models. Traditional data scientists often haven't had the opportunity to dive deep into the practical intricacies of LLMs—particularly advanced fine-tuning techniques, model compression strategies, memory optimization approaches, and specialized training workflows. This role requires a hands-on deep learning practitioner comfortable with modern frameworks and libraries specific to LLM development.

  • Enables domain-specific fine-tuning of models to client's unique utility context
  • Improves model performance while reducing computational costs through advanced optimization techniques
  • Creates Client-specific AI capabilities that address our unique operational challenges
  • Enables the CoE to move beyond generic AI tools to customized solutions that deliver higher business value

Key Responsibilities:

  • Implement and optimize advanced fine-tuning approaches (LoRA, PEFT, QLoRA) to adapt foundation models to client's domain
  • Develop systematic prompt engineering methodologies specific to utility operations, regulatory compliance, and technical documentation
  • Create reusable prompt templates and libraries to standardize interactions across multiple LLM applications and use cases
  • Implement prompt testing frameworks to quantitatively evaluate and iteratively improve prompt effectiveness
  • Establish prompt versioning systems and governance to maintain consistency and quality across applications
  • Apply model customization techniques like knowledge distillation, quantization, and pruning to reduce memory footprint and inference costs
  • Tackle memory constraints using techniques such as sharded data parallelism, GPU offloading, or CPU+GPU hybrid approaches
  • Build robust retrieval-augmented generation (RAG) pipelines with vector databases, embedding pipelines, and optimized chunking strategies
  • Design advanced prompting strategies including chain-of-thought reasoning, conversation orchestration, and agent-based approaches
  • Collaborate with the MLOps engineer to ensure models are efficiently deployed, monitored, and retrained as needed

Expected Skillset:

  • Deep Learning & NLP: Proficiency with PyTorch/TensorFlow, Hugging Face Transformers, DSPy, and advanced LLM training techniques
  • GPU/Hardware Knowledge: Experience with multi-GPU training, memory optimization, and parallelization strategies
  • LLMOps: Familiarity with workflows for maintaining LLM-based applications in production and monitoring model performance
  • Technical Adaptability: Ability to interpret research papers and implement emerging techniques (without necessarily requiring PhD-level mathematics)
  • Domain Adaptation: Skills in creating data pipelines for fine-tuning models with utility-specific content