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Phd Computer Science Jobs in Garland, TX (NOW HIRING)

USAA

Senior Marketing Data Scientist

Plano, TX · On-site

... PhD) in mathematics, computer science, statistics, economics, finance, actuarial sciences, science and engineering, or other similar quantitative discipline and 4 years of experience in predictive ...

Capital One

Principal Associate, Data Science

Plano, TX · On-site +1

$56K - $56K/yr

... Computer Science, or a related quantitative field and 5 years of experience in performing data ... PhD or foreign equivalent degree in the aforementioned fields. * Must pass company assessment.

Capital One

Principal Associate, Data Science

Plano, TX · On-site

$56K - $56K/yr

... Computer Science, or a related quantitative field and 5 years of experience in performing data ... PhD or foreign equivalent degree in the aforementioned fields. * Must pass company assessment.

Judge Group, Inc.

Data Scientist - IV

Dallas, TX · On-site

MS/ PhD in Computer Science, Statistics, Math, Economics, Engineering, Business Analytics, Data Science or a related field By providing your phone number, you consent to: (1) receive automated text ...

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Phd Computer Science information

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$54.6K

$80.3K

$94.7K

How much do phd computer science jobs pay per year?

As of Aug 22, 2026, the average yearly pay for phd computer science in Garland, TX is $80,298.00, according to ZipRecruiter salary data. Most workers in this role earn between $74,900.00 and $90,300.00 per year, depending on experience, location, and employer.

What is a PhD in computer science?

A PhD in Computer Science is the highest academic degree in the field, focused on advanced research and the creation of new knowledge in computing. It typically involves several years of coursework followed by original research culminating in a dissertation. Graduates often pursue careers in academia, research, or advanced industry roles that require deep technical expertise and problem-solving skills.

What are the key skills and qualifications needed to thrive as a PhD in computer science?

To thrive as a PhD in Computer Science, you need advanced expertise in algorithms, programming, and research methodologies, typically supported by a doctoral degree in computer science or a related field. Mastery of programming languages (such as Python, Java, or C++), data analysis tools, and familiarity with version control systems like Git are commonly required, along with experience in publishing academic research. Critical thinking, problem-solving, strong written and verbal communication, and perseverance are vital soft skills for success in research and collaboration. These skills and qualifications are essential for making significant contributions to the field, driving innovation, and effectively sharing knowledge with the academic and professional community.

What are some common challenges faced by PhD computer science students during their research?

PhD Computer Science students often encounter challenges such as defining a clear and impactful research problem, managing long-term projects with limited guidance, and coping with the pressure to publish in top-tier conferences or journals. Balancing coursework, teaching responsibilities, and research can also be demanding. Effective time management, networking with peers and mentors, and seeking regular feedback can help students navigate these challenges and achieve their academic goals.

Is a PhD worth it for computer science?

A PhD in computer science can lead to careers in research, academia, or specialized industry roles, often requiring advanced skills in algorithms, data analysis, and programming. While it offers opportunities for high-level positions and expertise, it typically involves several years of study and may not be necessary for most industry jobs, which often value practical experience and skills. The decision depends on career goals and the desire for research or teaching roles.

What can I do after a PhD in computer science?

A PhD in computer science prepares individuals for careers in academia, research, or advanced industry roles such as data scientist, machine learning engineer, or software architect. Graduates often pursue postdoctoral research, work in R&D departments, or obtain certifications in specialized tools and programming languages to enhance their expertise.

What jobs can I get with a PhD in computer science?

A PhD in computer science qualifies individuals for advanced roles such as research scientist, data scientist, machine learning engineer, or university professor. These positions often require strong analytical skills, programming expertise, and knowledge of algorithms, data structures, and AI tools. Graduates may work in academia, industry research labs, or technology companies focusing on innovation and development.

What are popular job titles related to Phd Computer Science jobs in Garland, TX?

For Phd Computer Science jobs in Garland, TX, the most frequently searched job titles are:

What job categories do people searching Phd Computer Science jobs in Garland, TX look for?

The top searched job categories for Phd Computer Science jobs in Garland, TX are:

What cities near Garland, TX are hiring for Phd Computer Science jobs?

Cities near Garland, TX with the most Phd Computer Science job openings:

Phd Computer Science jobs near you
Infographic showing various Phd Computer Science job openings in Garland, TX as of August 2026, with employment types broken down into 8% Internship, 77% Full Time, 6% Part Time, and 9% Contract. Highlights an 80% In-person, and 20% Remote job distribution, with an average salary of $80,298 per year, or $38.6 per hour.

Applied Scientist, Document Understanding

TempWorks Software Incorporated

Frisco, TX • On-site

$120 - $160/hr

Other

Posted 4 days ago

Job description

You hold a PhD or Master's in Computer Science, AI, NLP, or a related field, with 3+ years of post-degree industry experience shipping document understanding, information extraction, or knowledge graph systems into production. You have hands‑on depth across model development, distillation, evaluation, and deployment. You work independently and measure success by what ships and performs in production.

What You’ll Do
  • Design and deploy semantic chunking models for lengthy, non-uniformly structured legal documents with adjustable granularity across use cases.
  • Build document enrichment systems that classify documents according to legal and customer-defined taxonomies and extract rich metadata.
  • Develop LLM-based knowledge graph construction pipelines that extract and link citations, entities, and legal concepts across diverse legal content.
  • Build scalable synthetic data generation systems for model training, multi-hop query simulation, and hallucination-free answer generation.
  • Apply knowledge distillation techniques to compress large models into latency-constrained, production-ready SLMs.
  • Design evaluation frameworks — component-level and end-to-end — using expert annotation and synthetic data.
  • Drive independent technical decisions on chunking strategy, classification approach, knowledge extraction methods, and multi-document reasoning architecture.
  • Partner with engineering on delivery, reliability, and scale across multiple product lines.
  • Contribute to published research at venues such as ACL, EMNLP, ICLR, NeurIPS, SIGIR, and KDD, and to intellectual property.
Required Qualifications
  • PhD or Master's in Computer Science, AI, NLP, or a related field.
  • 3+ years of post-degree industry experience shipping document understanding, information extraction, or knowledge graph systems into production — not research-only experience.
  • Publication record at ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD, or equivalent.
  • Production Python and experience with PyTorch, Hugging Face Transformers, and DeepSpeed.
  • Hands‑on production depth required in:
  • Document layout analysis and semantic chunking beyond fixed-size or paragraph-based methods.
  • Hierarchical, multi-label document classification with domain-specific and customer-defined schemas.
  • Entity recognition and linking, relation extraction, citation parsing, and knowledge graph construction from unstructured text.
  • LLM-based information extraction, few-shot and multi-task learning, and post-training.
  • Knowledge distillation, model compression, and SLM deployment under latency constraints.
  • Synthetic data generation for NLP: query-answer generation with verification and scalable data augmentation.
  • Annotation workflow design and evaluation framework development for document understanding tasks.
Preferred Qualifications
  • Legal document understanding, legal information extraction, or legal AI applications.
  • Complex document structures common in legal content: nested hierarchies, cross-references, non-uniform formatting, and embedded elements.
  • Retrieval, QA, or analysis systems over large document collections.
  • Knowledge graph frameworks for legal or enterprise applications.
  • RAG and agentic workflows for enterprise knowledge systems.
  • AzureML or AWS SageMaker.
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