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Full Time Gpu Programming Jobs in Texas (NOW HIRING)

Topaz Labs

Software Engineer, AI Inference / HPC

Dallas, TX · On-site

$110K - $150K/yr

Experience with OpenCV, ffmpeg, GPU programming * Experience with the raw image camera pipeline and ... This is a full-time onsite role in Dallas, TX, and we will ask you to relocate if you're not in the ...

Topaz Labs

Software Engineer, AI Inference / HPC

Dallas, TX · On-site

$110K - $150K/yr

Experience with OpenCV, ffmpeg, GPU programming * Experience with the raw image camera pipeline and ... This is a full-time onsite role in Dallas, TX, and we will ask you to relocate if you're not in the ...

Samsung Electronics Co., Ltd.

Platform Engineer

Taylor, TX · On-site

$76K - $145K/yr

Position Summary The Platform Engineer will support and improve critical DevOps, AI, GPU, and ... Wellness incentives and MORE Eligible full-time employees (salaried or hourly) may also receive MBO ...

Samsung Electronics Co., Ltd.

DFx Engineer

Austin, TX · On-site

$180K - $270K/yr

You will be working on IP-level projects (GPU, system interconnect) in bleeding-edge processes that ... In addition, regular full-time employees (salaried or hourly) are eligible for MBO bonus ...

Verilab

Design Verification Engineer

Austin, TX · On-site

$134K - $164K/yr

Embedded programming for ARM, or GPU processors. * Python or Perl. Benefits: Benefits * Full-time permanent employee with competitive salary. * 20 days paid vacation and observed holidays. * 401k ...

Apptronik

Senior SimOps Engineer

Austin, TX · On-site

$103K - $142K/yr

... GPU-accelerated HPC clusters on cloud platforms (e.g., AWS/GCP) or on-prem systems. • Advanced ... full-time experience in simulation or infrastructure engineering, with a track record of improving ...

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Full Time Gpu Programming information

What are some typical challenges faced by professionals in full-time GPU programming roles, and how can they be addressed?

Full-time GPU programmers often encounter challenges such as optimizing code for parallel execution, managing memory efficiently, and debugging complex kernels. These tasks require a deep understanding of GPU architecture and tools like CUDA or OpenCL. Collaborating closely with other developers, data scientists, and hardware engineers is essential to identify performance bottlenecks and ensure that solutions meet project requirements. Staying updated with the latest advancements and regularly profiling code can help address these challenges effectively.

What are the key skills and qualifications needed to thrive as a Full Time GPU Programmer, and why are they important?

To thrive as a Full Time GPU Programmer, you need strong programming skills in languages like C++ and Python, a deep understanding of parallel computing, and often a degree in computer science or a related field. Familiarity with GPU programming frameworks such as CUDA or OpenCL, and experience with performance profiling tools, are typically required. Problem-solving ability, attention to detail, and effective teamwork distinguish top performers in this role. These skills ensure efficient development of high-performance, scalable applications that leverage GPU capabilities for computational tasks.

What is the difference between Full Time Gpu Programming vs Gpu Software Engineer?

AspectFull Time Gpu ProgrammingGpu Software Engineer
Required CredentialsBachelor's or higher in Computer Science, Engineering, or related field; experience with GPU programming languagesSimilar credentials; often requires experience with CUDA, OpenCL, or Vulkan
Work EnvironmentPrimarily focused on developing and optimizing GPU code, often in research or high-performance computing settingsDesigning, developing, and maintaining GPU-based software applications in industry or research
Employer & Industry UsageTech companies, research labs, gaming, and scientific computingTech firms, gaming, AI, and scientific industries

Both roles involve GPU programming skills, but Full Time Gpu Programming emphasizes writing and optimizing GPU code, while Gpu Software Engineer focuses on developing complete GPU-accelerated software solutions. The roles often overlap but differ in scope and application.

What is a Full Time GPU Programmer?

A Full Time GPU Programmer is a software developer who specializes in writing and optimizing code to run on Graphics Processing Units (GPUs). These professionals use languages like CUDA or OpenCL to leverage the parallel processing capabilities of GPUs for tasks such as scientific computing, machine learning, graphics rendering, and more. They work on designing, implementing, and debugging GPU-accelerated algorithms to improve performance and efficiency in various applications. Full Time GPU Programmers often collaborate with other engineers and researchers to integrate GPU solutions into larger software systems.
What are the most commonly searched types of Gpu Programming jobs in Texas? The most popular types of Gpu Programming jobs in Texas are:
What are popular job titles related to Full Time Gpu Programming jobs in Texas? For Full Time Gpu Programming jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Full Time Gpu Programming jobs in Texas look for? The top searched job categories for Full Time Gpu Programming jobs in Texas are:
Full Time Gpu Programming jobs near you
Infographic showing various Full Time Gpu Programming job openings in Texas as of July 2026, with employment types broken down into 76% Full Time, 22% Part Time, and 2% Contract. Highlights an 96% Physical, 1% Hybrid, and 3% Remote job distribution.
CUDA Senior Developer ( Mathematical Mindset/ New Grads

CUDA Senior Developer ( Mathematical Mindset/ New Grads

Ktek

Dallas, TX • On-site

Full-time

Posted 10 days ago

Job description

Position/ TITLE: CUDA Developers Junior to senior & Lead Level ( Mathematical Mindset/ New Grads)
Location : New yOrk, NJ, Dallas TX, Florida
Open for Fulltime & Contract
Please share resumes at Chandra.reddy@ktekresourcing.com

Who are we looking For:
We are seeking talented individuals with 3 to 8 years of experience who are passionate about mathematics and eager to learn CUDA programming to join our innovative team. While previous CUDA experience is beneficial, it is not mandatory to prioritize strong mathematical abilities, analytical thinking, and enthusiasm for learning and growth in high-performance computing.
Key Responsibilities
Learn and apply CUDA programming principles to develop and optimize GPU-accelerated applications.
Good team player who will work with team to contribute to design. He will work to implement, and debug parallel algorithms.
Analyze performance bottlenecks and optimize computations for maximum efficiency.
Document code, algorithms, and procedures clearly for internal and external stakeholders.
Technical Skills Must Have
Strong mathematical foundation in areas such as linear algebra, calculus, numerical methods, or statistics.
Proficiency in at least one programming language (C++, Python, or similar).
Excellent problem-solving skills and logical reasoning capabilities.
Ability to learn quickly and adapt to new technologies.
Technical Skills - Good to have
Familiarity with parallel computing concepts.
Exposure to GPU programming (CUDA/OpenCL).
Having Worked in Cuda Frameworks like RAPIDS, cuDF, cuTensor, PyTorch, TensorFlow is a plus.
Experience with scientific computing, numerical simulation and/or linear programming.
What We Offer
Opportunity to gain expertise in cutting-edge CUDA technology.
Supportive learning environment with mentorship from experienced CUDA developers.
Competitive salary and benefits.
Flexible working conditions (including remote work options).
Mphasis is an equal-opportunity employer. We value diversity and encourage candidates from all backgrounds to apply.
Application
Interested candidates should submit their resume and a brief statement highlighting their mathematical expertise, programming skills, and interest in CUDA development.
Please apply along with your answer to the following problem. Any application without an answer to the problem will not be entertained.
Mathematical Challenge:
Include your proposed approach or insights on the following linear algebra problem in your application:
'Given a very large sparse matrix, propose an efficient algorithm or method to quickly identify if the matrix is singular or invertible without performing full matrix inversion.'
Preferred Qualifications:
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field.
A portfolio showcasing CUDA experience.
Process Skills:
General SDLC processes
Understanding of utilizing Agile and Scrum software development methodologies
Skill in gathering and documenting user requirements and writing technical specifications.
Behavioral Skills:
Work closely with designers, Architects, data scientists, product managers, and other engineers to deliver comprehensive solutions.
Strong problem-solving skills, with a creative approach to tackling complex challenges.
Communicate effectively and share technical knowledge with the team.
Be open to feedback and continuously learn and adapt to new technologies.
Ability to work independently and as part of a team.
Passion for learning and staying updated on the latest technologies.
Good Attitude and Quick learner .
Certification (Good to have) :
Related to CUDA any certifications
AI/ML or Python certification.