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Natural Language Processing Jobs in Austin, TX (NOW HIRING)

SpyCloud

Senior Data Scientist, Applied ML

Austin, TX · On-site +1

$154K - $200K/yr

Demonstrated experience leveraging Natural Language Processing (NLP) techniques for text classification, tagging, or entity extraction * Proficiency in Python and key ML libraries: PyTorch ...

Future Secure AI

AI Engineer, Software

Austin, TX · On-site

Required : • 7 years of software development experience • Experience with and/or knowledge of AI concepts including natural language processing, machine learning, and neural networks • Strong ...

Apple Inc.

Gate-level IR/EM CAD/NLP Engineer

Austin, TX · On-site

$125 - $150/hr

Experience in writing machine learning algorithms, implementing natural language processing (NLP), or using large language models in industry applications * Experience in EDA Tool, CAD flow or TFM ...

Apple

Gate-level IR/EM CAD/NLP Engineer

Austin, TX · On-site

... natural language processing (NLP), or using large language models in industry applications Experience in EDA Tool, CAD flow or TFM (Tool Flow Methodology) Proficiency with UNIX shell environment ...

Apple

Gate-level IR/EM CAD/NLP Engineer

Austin, TX · On-site

... natural language processing (NLP), or using large language models in industry applications Experience in EDA Tool, CAD flow or TFM (Tool Flow Methodology) Proficiency with UNIX shell environment ...

Dell GmbH

Senior Advisor, Data Science

Round Rock, TX · On-site

$150 - $200/hr

Experience working with Graph databases, taxonomies, and Natural Language Processing strategies (NLP) for optimization, knowledge of Taxonomy and Ontology Management Systems tools like GraphWise ...

SpyCloud

Senior Data Scientist, Applied ML

Austin, TX · On-site +1

$154K - $200K/yr

Demonstrated experience leveraging Natural Language Processing (NLP) techniques for text classification, tagging, or entity extraction * Proficiency in Python and key ML libraries: PyTorch ...

Sourceability

Principal NLP Scientist

Austin, TX · On-site

The Principal NLP Scientist is a senior technical leader responsible for designing, researching, and improving advanced Natural Language Processing and Large Language Model capabilities for ...

SSOE, Inc.

Structural Engineer

Austin, TX · On-site

$80 - $100/hr

Agentic Automation Empowering Teams with AI-Driven Workflows SSOE is actively piloting AI Chat large language models (LLMs) or copilots that use Natural Language Processing (NLP) to streamline both ...

Apple

Senior Machine Learning Engineer

Austin, TX · On-site

$121K - $160K/yr

Experience in Recommender Systems, Personalization, Search, Computational Advertising or Natural Language Processing including RAG based Generative AI and transformer architecture. Skilled in ...

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Natural Language Processing information

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How much do natural language processing jobs pay per hour?

As of Sep 8, 2026, the average hourly pay for natural language processing in Austin, TX is $25.25, according to ZipRecruiter salary data. Most workers in this role earn between $17.40 and $29.33 per hour, depending on experience, location, and employer.

What is a natural language processing?

A Natural Language Processing (NLP) job involves developing and improving algorithms that enable computers to understand, interpret, and generate human language. Professionals in this field work on tasks like speech recognition, text analysis, machine translation, and chatbot development. They often use machine learning, deep learning, and linguistic principles to build and refine NLP models. NLP experts commonly work in industries such as healthcare, finance, and technology to enhance communication and automate language-related tasks.

What are the key skills and qualifications needed to thrive in natural language processing, and why are they important?

To thrive in Natural Language Processing, you need strong expertise in linguistics, statistics, and machine learning, typically supported by a degree in computer science, computational linguistics, or a related field. Familiarity with tools and frameworks such as Python, TensorFlow, PyTorch, spaCy, and NLP libraries, as well as certifications in data science or NLP, are valuable assets. Analytical thinking, problem-solving skills, and the ability to collaborate across multidisciplinary teams are highly desirable. These competencies are essential for developing powerful language models, extracting meaningful insights from data, and delivering effective real-world solutions in language technology.

What are some typical challenges faced by professionals in natural language processing?

Professionals in Natural Language Processing (NLP) often encounter challenges such as understanding ambiguities in human language, managing large and unstructured datasets, and keeping up with rapid advances in NLP methodologies. They may also need to fine-tune models for domain-specific contexts and ensure solutions meet ethical and privacy guidelines. Collaboration with data scientists, linguists, engineers, and product teams is common, requiring strong communication skills. Successfully tackling these challenges is a critical part of developing robust NLP applications that add meaningful value to users and businesses.

How to get a job in Natural Language Processing?

To get a job in Natural Language Processing (NLP), candidates typically need a strong background in computer science, linguistics, or related fields, along with proficiency in programming languages like Python and experience with NLP libraries such as NLTK or spaCy. Gaining practical experience through projects, internships, or research, and obtaining relevant certifications can improve employability. A solid understanding of machine learning, deep learning, and data analysis is also beneficial for NLP roles.

Is natural language processing a good career?

Natural Language Processing (NLP) is a growing field within artificial intelligence that involves developing algorithms to understand and generate human language. It offers opportunities in industries such as tech, healthcare, and finance, often requiring skills in machine learning, programming, and linguistics. The demand for NLP professionals is increasing, making it a promising career choice for those interested in AI and language technologies.

What can I do with natural language processing?

A natural language processing (NLP) professional develops systems that enable computers to understand, interpret, and generate human language. This includes tasks like sentiment analysis, language translation, chatbots, and speech recognition, often using tools like Python, NLP libraries, and machine learning techniques. NLP roles require strong programming skills and knowledge of linguistics or data science.

What are popular job titles related to Natural Language Processing jobs in Austin, TX?

For Natural Language Processing jobs in Austin, TX, the most frequently searched job titles are:

What job categories do people searching Natural Language Processing jobs in Austin, TX look for?

The top searched job categories for Natural Language Processing jobs in Austin, TX are:

Natural Language Processing jobs near you
Infographic showing various Natural Language Processing job openings in Austin, TX as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 20% Part Time, 1% Temporary, and 3% Contract. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution, with an average salary of $52,521 per year, or $25.3 per hour.

Senior Data Scientist, Applied ML

SpyCloud

Austin, TX • On-site, Remote

$154K - $200K/yr

Full-time

Re-posted 4 days ago

Job description

We're looking for a Senior Data Scientist, Applied ML to design, build, and deploy models for critical cybersecurity use cases like incident detection and mitigation, fraud intelligence, and risk scoring.

You'll own the full model lifecycle - from data understanding and preparation through prototyping and deployment in production - and work closely with engineering, product, and research teams to turn complex problems into scalable, reliable systems. This role is ideal for someone who thrives in applied, hands-on environments where impact and collaboration matter, and who has genuinely owned data work end-to-end.

What You'll Do:

You will develop, train, and deploy models using real-world structured and unstructured data to power critical security features such as threat detection and alerting, entity resolution and risk scoring, and natural language-based tagging and classification. You'll build the preprocessing and feature engineering pipelines your own models depend on, and you'll own model monitoring and evaluation, designing feedback loops to continuously improve accuracy and effectiveness.

You'll be equally comfortable prototyping new approaches from scratch and taking existing prototypes - from our R&D team or your own experimentation - to production-grade reliability. This role sits deliberately at the intersection of research and deployment, not on one side of it: you'll take ownership of data validation, transformation, and pipeline health across the handoff points between research and production, not just within the boundaries of your own models.

Working closely with software and data engineers, you'll help productionize models in modern cloud-native environments like AWS.

This role is highly collaborative. You'll partner with product managers and domain experts to define success criteria, rapidly prototype MVPs to test new features or signals, and work with the data engineering team to access and understand diverse data sources, owning the transformation and validation steps throughout. Your input will also contribute to broader system design and architectural decisions.

Strong communication and documentation skills are essential. You will clearly articulate model design choices, tradeoffs, and outcomes to both technical and non-technical stakeholders, maintain thorough documentation for models, pipelines, and evaluation methodologies, and participate in model and compliance reviews and customer-facing discussions as needed.

Requirements:

  • 4+ years of experience building and shipping models in production with direct, hands-on ownership of the data lifecycle around them
  • Strong background in applied math (linear algebra, optimization, statistics) and machine learning
  • Demonstrated experience leveraging Natural Language Processing (NLP) techniques for text classification, tagging, or entity extraction
  • Proficiency in Python and key ML libraries: PyTorch, TensorFlow, scikit-learn, XGBoost
  • Demonstrated experience building or maintaining data/feature pipelines (e.g., with Airflow, Spark, Pandas) as part of your own modeling work
  • Comfort with model versioning and monitoring in production (e.g., MLflow, DVC) 
  • Working experience deploying models into cloud environments or containerized services
  • Strong communication skills and the ability to translate complex problems into actionable solutions

Nice to Have:

  • Deeper MLOps/DevOps/data engineering exposure: infra-as-code, CI/CD depth, etc.
  • Familiarity with cybersecurity datasets or domains: threat intelligence, account takeover, ransomware, etc.
  • Exposure to graph analytics, knowledge graphs, or cybersecurity frameworks like MITRE ATT&CK
  • Background working with unstructured data (e.g., log files, threat reports, breach datasets)

Base Salary Range: $154,000 - $200,000

The salary range reflects the expected base compensation for a fully qualified candidate at this level based on experience, qualifications, and market data at the time of posting.

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