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Cuda Engineer Jobs in Texas (NOW HIRING)

Nvidia

Senior DL Compiler Engineer -CUDA Tile

Austin, TX · On-site

$121K - $160K/yr

We are hiring software engineers for the CUDA Tile team. NVIDIA GPUs are at the center of the deep learning revolution and continue to enable breakthroughs in generative AI, large language models ...

Nvidia

Senior DL Compiler Engineer -CUDA Tile

Austin, TX · On-site

$121K - $160K/yr

We are hiring software engineers for the CUDA Tile team. NVIDIA GPUs are at the center of the deep learning revolution and continue to enable breakthroughs in generative AI, large language models ...

Raytheon

Software Engineer II -Algorithms (Onsite)

Plano, TX · On-site

$93K - $128K/yr

The Algorithms Software Engineer II will be responsible for implementing system-level architecture ... C++, and CUDA code. • Model and test new algorithms in high-performance computing (HPC ...

Nvidia

Senior Compiler Engineer - Rust GPU

Austin, TX

$121K - $160K/yr

We are redefining how developers write high-performance GPU software by bringing the safety, expressiveness, and modern tooling of Rust to native GPU and CUDA development. On this team, you will ...

Nvidia

Senior GPU Compiler Development Engineer

Austin, TX · On-site

$121K - $160K/yr

Work with NVIDIA GPU Architecture and CUDA Programming model teams to build abstractions to expose new GPU features in portable and performant ways in PTX ISA. PTX Compiler (PTXAS) apart from ...

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All JobsCuda Engineer JobsCuda Engineer Jobs in Texas

Cuda Engineer information

See Texas salary details

$34K

$99.9K

$128.1K

How much do cuda engineer jobs pay per year?

As of Jul 28, 2026, the average yearly pay for cuda engineer in Texas is $99,949.00, according to ZipRecruiter salary data. Most workers in this role earn between $82,500.00 and $126,700.00 per year, depending on experience, location, and employer.

What are CUDA Engineers?

CUDA Engineers are software developers who specialize in using NVIDIA's CUDA (Compute Unified Device Architecture) platform to write programs that run on Graphics Processing Units (GPUs). They optimize and accelerate computational tasks by parallelizing code, making use of GPUs’ capabilities for high-performance computing. CUDA Engineers often work in fields like machine learning, scientific computing, and graphics, where large amounts of data need to be processed quickly. Their expertise includes proficiency in C/C++, CUDA programming, and understanding GPU hardware and parallel computing concepts.

What is the difference between Cuda Engineer vs GPU Developer?

AspectCuda EngineerGPU Developer
Required CredentialsBachelor's or Master's in Computer Science, Engineering, or related; knowledge of CUDA, C++, parallel programmingBachelor's or Master's in Computer Science, Engineering, or related; experience with GPU programming, CUDA, OpenCL
Work EnvironmentResearch labs, tech companies, hardware firms focusing on GPU accelerationSoftware development teams, gaming, AI, scientific computing sectors
Employer & Industry UsageHardware manufacturers, AI companies, high-performance computing firmsGame development, scientific research, machine learning applications

While both roles involve GPU programming and CUDA expertise, a Cuda Engineer primarily focuses on developing and optimizing CUDA-based solutions for hardware acceleration. In contrast, a GPU Developer works on broader GPU programming tasks, including application development across various platforms. The roles often overlap but differ in scope and specific focus areas.

What are some common challenges faced by CUDA Engineers when optimizing GPU-accelerated applications?

CUDA Engineers frequently encounter challenges such as managing memory effectively between the host and the device, optimizing kernel performance, and minimizing data transfer bottlenecks. Debugging parallel code can also be complex due to race conditions and the difficulty of reproducing timing-related bugs. Collaborating closely with software developers and data scientists is essential to ensure that GPU resources are leveraged efficiently and that the application's overall performance meets project goals.

What are the key skills and qualifications needed to thrive as a CUDA Engineer, and why are they important?

To thrive as a CUDA Engineer, you need a strong proficiency in C/C++ programming, parallel computing concepts, and deep knowledge of GPU architectures, often supported by a computer science or engineering degree. Experience with NVIDIA CUDA Toolkit, profiling/debugging tools, and sometimes certifications like NVIDIA DLI are highly valuable. Strong problem-solving, attention to detail, and effective communication skills help you optimize code and collaborate across teams. These skills ensure efficient development of high-performance GPU applications and successful project delivery in compute-intensive fields.
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Cuda Engineer jobs near you
Infographic showing various Cuda Engineer job openings in Texas as of July 2026, with employment types broken down into 3% As Needed, 65% Full Time, 3% Part Time, 18% Temporary, 1% Contract, and 10% Nights. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $99,949 per year, or $48.1 per hour.
CUDA Senior Developer ( Mathematical Mindset/ New Grads

CUDA Senior Developer ( Mathematical Mindset/ New Grads

Ktek

Dallas, TX • On-site

Full-time

Posted 12 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.