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Senior Natural Language Processing Engineer Jobs

Overture Partners

Prompt Engineer- Pipeline

Our team values creativity, expertise in natural language processing, and a passion for developing intelligent and responsive AI systems. We are looking for a talented Prompt Engineer to join our ...

Jobtailor

Forward Deployed Engineer

California, MO · On-site

$95 - $135/hr

Apply natural language processing techniques to improve text analytics * Participate in client ... Strong foundation in software engineering principles, application development, and system design

SonSoft Inc.

Sr. Data Scientist

Newark, NJ · On-site

They are seeking a Senior Data Scientist with extensive experience in machine learning algorithms and strong skills in Text Mining and Natural Language Processing. Responsibilities : • 10 to 15 ...

Teklead Corp.

Sr Chatbot Engineer

Westlake Village, CA · On-site

$68 - $78/hr

... Sr. Chatbot Engineer to join an Artificial Intelligence team. Join our Tech hub and work in a ... Description: • Work on state of the art Machine Learning, Natural Language Processing and Gen AI ...

CoVar

Signal Processing Engineer

Bahama, NC · On-site

Signal Processing Engineer About CoVar CoVar is a small AI/ML R&D software company in Durham, NC ... and natural language processing. * Spectrum monitoring and signal classification using machine ...

CoVar

Signal Processing Engineer

Durham, NC

Signal Processing Engineer About CoVar CoVar is a small AI/ML R&D software company in Durham, NC ... and natural language processing. * Spectrum monitoring and signal classification using machine ...

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Senior Natural Language Processing Engineer information

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$75K

$121.1K

$170.5K

How much do senior natural language processing engineer jobs pay per year?

As of Sep 4, 2026, the average yearly pay for senior natural language processing engineer in the United States is $121,116.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,000.00 and $134,500.00 per year, depending on experience, location, and employer.

What does a senior natural language processing engineer do?

A Senior Natural Language Processing (NLP) Engineer designs and implements advanced algorithms that enable computers to understand, interpret, and generate human language. They work on tasks such as text classification, sentiment analysis, machine translation, and conversational AI. In addition to developing NLP models, they often lead projects, mentor junior team members, and collaborate with data scientists, software engineers, and product managers to build and deploy language-based applications. Their expertise helps organizations leverage language data to solve complex problems and improve user experiences.

What are the key skills and qualifications needed to thrive as a senior natural language processing engineer, and why are they important?

To thrive as a Senior Natural Language Processing Engineer, you need a deep understanding of machine learning, linguistics, and advanced programming skills in languages like Python, typically backed by a degree in computer science or a related field. Familiarity with NLP frameworks (such as spaCy, NLTK, or Hugging Face), cloud platforms, and experience with deep learning libraries like TensorFlow or PyTorch are crucial. Strong problem-solving abilities, effective communication, and the ability to work collaboratively in multidisciplinary teams are standout soft skills. These skills and qualities are essential for developing, deploying, and refining language-based AI solutions that meet complex business and user needs.

What are some common challenges faced by senior natural language processing engineers when deploying NLP models to production?

Senior NLP Engineers often encounter challenges such as ensuring model scalability, maintaining accuracy with real-world data, and addressing data privacy concerns. Deploying models at scale requires optimizing for speed and efficiency, as well as monitoring performance to handle domain shifts or unexpected inputs. Collaboration with DevOps and data engineering teams is crucial to integrate models seamlessly into existing pipelines and to ensure robust, maintainable solutions.

What is the difference between Senior Natural Language Processing Engineer vs Data Scientist?

AspectSenior Natural Language Processing EngineerData Scientist
Required CredentialsAdvanced degree in CS, NLP, or related field; experience with NLP frameworksDegree in CS, statistics, or related; data analysis skills
Work EnvironmentDevelops NLP models, algorithms, and language-specific toolsAnalyzes data, builds predictive models, visualizes insights
Employer & Industry UsageTech companies, AI startups, research institutions focusing on language techVarious industries including finance, healthcare, marketing

While both roles require strong analytical skills and programming knowledge, Senior NLP Engineers specialize in language-specific models and algorithms, whereas Data Scientists focus on broader data analysis and predictive modeling across various data types.

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Senior Natural Language Processing Engineer jobs near you
Infographic showing various Senior Natural Language Processing Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 74% Full Time, 21% Part Time, 1% Temporary, and 3% Contract. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution, with an average salary of $121,116 per year, or $58.2 per hour.

Senior Data Scientist

Fidelity Investments

Westlake, TX • On-site

Full-time

Re-posted 18 days ago

Fidelity Investments rating

8.7

Company rating: 8.7 out of 10

Based on 273 frontline employees who took The Breakroom Quiz

16th of 154 rated financial services

Job description


Senior Data Scientist - Applied AI, NLP, and LLM Solutions
Note: Fidelity will not provide immigration sponsorship for this position
Fidelity Workplace Investing is seeking hands-on, builder-oriented Senior Data Scientists with experience in applied AI, natural language processing, large language models, machine learning, and knowledge graph technologies. This position will be based full time in either Westlake, TX or Merrimack, NH.
The Purpose of Your Role
This individual will lead high-profile applied data science and artificial intelligence initiatives across Workplace Investing, working closely with Technology, Product Management, AI/ML Engineering, and others. The role will focus on developing and evaluating AI-based solutions using natural language processing (NLP), large language models (LLM), machine learning (ML), knowledge graphs, agentic AI patterns, and other advanced or emerging techniques. Key assignments may include document processing and information extraction, schema mapping, enterprise assistants, recommender systems, and anomaly detection.
The successful candidate must be comfortable operating in a fast-paced and sometimes ambiguous environment working with current and emerging AI technologies. They will be expected to gather and analyze data from multiple structured and unstructured data sources, develop reliable models and evaluation frameworks, interpret and clearly communicate findings to technical and business audiences. They will support a broad range of applied AI initiatives with the highest degree of quality, partner effectively with engineering teams to move solutions into production, and thrive in a high-performing, collaborative work environment. The ideal candidate combines strong data science fundamentals with product instincts, technical curiosity, and a track record of delivering measurable business impact.
The Skills You Bring
  • PhD in Computer Science, Information Science, Statistics, or a related STEM discipline with focus on AI, machine learning, natural language processing, deep learning, knowledge graphs, or related methods; OR a Master's Degree in a related field with 3 or more years relevant professional experience
  • Strong technical foundation in machine learning and statistical modeling, with deeper experience in one or more applied AI areas such as natural language processing, large language models, deep learning, knowledge graphs, or related methods.
  • Strong Python and SQL programming skills with demonstrated proficiency in data extraction, data engineering, exploratory analysis, feature engineering, data modeling, pipeline automation, and model evaluation.

  • Solid verbal communication, presentation, and technical writing skills with an ability to explain complex data science, statistics, and computer science concepts clearly to nontechnical audiences.
  • Experience or working knowledge in one or more applied AI areas such as information retrieval, question answering, chatbot evaluation, retrieval-augmented generation, or agentic AI frameworks.
  • Exposure to intelligent document processing use cases, which may include document classification, OCR, key-value extraction, signature or seal detection, annotation strategy and dataset creation, and evaluation of extraction quality.
  • Working knowledge of embedding models, vector representations, semantic similarity clustering, or dimensionality reduction techniques such as t-SNE or UMAP.
  • Experience in one or more predictive modeling areas such as recommendation systems, ranking models, ensemble methods, anomaly detection, statistical process control, time-series monitoring, threshold strategies, or alert-quality evaluation.
  • Experience designing or contributing to AI/ML evaluation and monitoring frameworks, including benchmark datasets, labeled and synthetic test data, model and prompt comparison, precision/recall analysis, error analysis, latency assessment, cost-quality tradeoff analysis, and production monitoring with tools such as Fiddler.

The Value You Deliver
  • Lead the data science and model development components of projects involving large language models, natural language processing, knowledge graphs, and related applied techniques.
  • Design, build, and deploy applied AI solutions across NLP, LLMs, document processing, schema mapping, recommendation, and anomaly detection use cases.
  • Lead data analysis with diverse scope and complex business and technical challenges
  • Develop best practices for data science, considering the full analytical lifecycle
  • Ensure the delivery of high-quality, trustworthy data science by developing guidelines and rigorous evaluation frameworks for AI/ML solutions.
  • Implement new technologies in a production environment with product, IT, and data engineering teams
  • Present reports and findings to senior-level technical and nontechnical audiences

How Your Work Impacts the Organization
As a data scientist in Fidelity Workplace Investing, you will contribute to advancing the analytics and data science capability for a variety of employee benefit products and will take the organization to the next level.
Fidelity's Onsite Working Model
Fidelity is transitioning to a full-time onsite working model through a phased rollout across regions and roles. Currently, some roles and locations require 100% onsite presence, while others require less. Onsite expectations are likely to evolve as the rollout continues. This transition does not apply to fully remote roles.
Certifications:
Category:
Data Analytics and Insights
Please be advised that Fidelity's business is governed by the provisions of the Securities Exchange Act of 1934, the Investment Advisers Act of 1940, the Investment Company Act of 1940, ERISA, numerous state laws governing securities, investment and retirement-related financial activities and the rules and regulations of numerous self-regulatory organizations, including FINRA, among others. Those laws and regulations may restrict Fidelity from hiring and/or associating with individuals with certain Criminal Histories.

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