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Machine Learning Nlp Jobs in Texas (NOW HIRING)

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Machine Learning Nlp information

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$34.9K

$114.3K

$183.1K

How much do machine learning nlp jobs pay per year?

As of Aug 18, 2026, the average yearly pay for machine learning nlp in Texas is $114,350.00, according to ZipRecruiter salary data. Most workers in this role earn between $91,800.00 and $126,700.00 per year, depending on experience, location, and employer.

What is a machine learning NLP?

A Machine Learning NLP job involves developing algorithms and models that enable machines to understand, process, and generate human language. Professionals in this role work with large datasets, train models on text data, and fine-tune natural language processing techniques such as sentiment analysis, text classification, and language translation. They often use machine learning frameworks like TensorFlow, PyTorch, and NLP libraries such as spaCy or Hugging Face Transformers. The goal is to build intelligent applications, including chatbots, search engines, and automated content analysis systems.

What does a machine learning NLP do?

As a Machine Learning NLP specialist, your daily responsibilities often include designing and implementing NLP models, cleaning and preprocessing large text datasets, and experimenting with algorithms to improve model performance. You may also evaluate model results, collaborate with software engineers and data scientists, and stay updated on the latest research in the field. Frequent code reviews, participation in team meetings, and contributing to documentation are also common. This role combines hands-on technical work with collaborative problem-solving to develop language-based AI solutions for real-world applications.

What are the key skills and qualifications needed to thrive in the machine learning NLP position?

To thrive as a Machine Learning NLP professional, you need a strong background in machine learning, natural language processing, data analysis, and proficiency in programming languages such as Python, typically supported by a relevant degree in computer science or related field. Familiarity with NLP libraries (like spaCy, NLTK, or Hugging Face), machine learning frameworks (such as TensorFlow or PyTorch), and experience with cloud platforms are highly valued, and certifications can enhance your profile. Strong problem-solving skills, effective communication abilities, and adaptability are important soft skills in this role. These competencies enable you to build sophisticated language models and efficiently collaborate on cross-functional projects in a rapidly evolving technical landscape.

Are machine learning NLP engineers in demand?

Machine learning NLP engineers are in high demand due to the growing use of natural language processing in applications like chatbots, virtual assistants, and data analysis. Companies seek professionals skilled in deep learning frameworks, Python, and NLP tools to develop and improve AI language models, making this a strong career field with positive job growth prospects.

What are popular job titles related to Machine Learning Nlp jobs in Texas?

For Machine Learning Nlp jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Machine Learning Nlp jobs in Texas look for?

The top searched job categories for Machine Learning Nlp jobs in Texas are:

Machine Learning Nlp jobs near you
Infographic showing various Machine Learning Nlp job openings in Texas as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 20% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $114,350 per year, or $55 per hour.

IT - Technology Lead | data science | Machine Learning

Spruce Infotech

Austin, TX • On-site

Full-time

Re-posted 29 days ago

Job description

Client: Apple
Do you have a pre-identified candidate? New sourcing? Existing Subcon?- New sourcing
Max Hourly Pay Rate to the candidate?: XXX/Hr
Do you have a preferred interview schedule?: Yes
Would you require the candidates to meet you for an in-person interview?: No
Is Skype/WebEx interview, OK?: Yes
Work Location with ZIP: Austin or Raleigh
Is remote work an option?: No
If remote work, please indicate how many days are required to be in the office and remote: 3 days in a week required to be in the office
Chance to be perm?: Yes
Performance Expectations:
Technical Hiring Criteria (Must Haves)
• Top 3 Required skills: Agentic AI, Machine Learning, Data science, SQL
• Years of experience in each of the must-have skills: 5+ Years
• Any Certifications required: No
Any additional information you would like to share about the project specs/nature of work: NA
Job Description-
Responsibilities:
• Understand the data needs of stakeholder teams in terms of key data models and reporting, and translate that into technical requirements
• Define, build and manage key data pipelines in dbt that transform raw logs into canonical datasets
• Establish high data integrity standards and SLAs to ensure timely, accurate delivery of data
• Develop insightful and reliable dashboards to track performance of core metrics that will deliver insights to the whole company
• Build foundational data products, dashboards and tools to enable self-serve analytics to scale across the company
• Influence the future roadmap of Product and GTM teams from a data systems perspective
• Become an expert in our organization's data models and the company's data architecture
You might be a good fit if you have:
• 5+ years of experience as an Analytics Data Engineer or similar Data Science & Analytics roles, preferably partnering with GTM and Product leads to build and report on key company-wide metrics.
• A passion for the company's mission of building helpful, honest, and harmless AI.
• Expertise in building multi-step ETL jobs, building robust data models through tooling like dbt; proficiency with workflow management platforms like Airflow and version control management tools through GitHub.
• Expertise in SQL and Python to transform data into accurate, clean data models.
• Experience building data reporting and dashboarding in visualization tools like Hex to serve multiple cross-functional teams.
• A bias for action and urgency, not letting perfect be the enemy of the effective.
• A "full-stack mindset", not hesitating to do what it takes to solve a problem end-to-end, even if it requires going outside the original job description.
• Experience building an Analytics Data Engineering (or similar) function at start-ups.
• A strong disposition to thrive in ambiguity, taking initiative to create clarity and forward progress.
Key Responsibilities
• Prompt Engineering Excellence: Design, test, and optimize system prompts and feature-specific prompts that shape Claude's behavior across consumer and API products.
• Evaluation Development: Build and maintain comprehensive evaluation suites that ensure model quality and consistency across product launches and updates.
• Cross-functional Collaboration: Partner closely with product teams, research teams, and safeguards to ensure new features meet quality and safety standards.
• Model Launch Support: Play a critical role in model releases, ensuring smooth rollouts and catching regressions before they impact users.
• Infrastructure Contribution: Help build and improve the frameworks and tools that allow teams to develop and test prompts and features with confidence.
• Knowledge Transfer: Mentor product engineers on prompt engineering best practices and help teams build their first evaluations.
• Rapid Iteration: Work in a fast-paced environment where model capabilities advance daily, requiring quick adaptation and creative problem-solving.
Required Qualifications
• 5+ years of software engineering experience with Python or similar languages.
• Demonstrated experience with LLMs and prompt engineering (through work, research, or significant personal projects).
• Strong understanding of evaluation methodologies and metrics for AI systems.
• Excellent written and verbal communication skills - you'll need to explain complex model behaviors to diverse stakeholders.
• Ability to manage multiple concurrent projects and prioritize effectively.
• Experience with version control, CI/CD, and modern software development practices.
Preferred Qualifications
• Experience with Claude or other frontier AI models in production settings.
• Background in machine learning, NLP, or related fields.
• Experience with A/B testing and experimentation frameworks (e.g., Statsig).
• Familiarity with AI safety and alignment considerations.
• Experience building tools and infrastructure for ML/AI workflows.
• Track record of improving AI system performance through systematic evaluation and iteration.

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