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Cuda Programmer Jobs in Texas (NOW HIRING)

NVIDIA

Senior HPC Storage Engineer

Austin, TX · On-site

... GPUs, CUDA programming, and NCCL, including performance benchmarking via MLPerf. • Deep familiarity with storage hardware (HDDs, SSDs, NVMe), enclosures, and specialized appliances like Network ...

Spectral MD Inc

Software Engineer

Dallas, TX · On-site

Coordinate CUDA ↔ OpenGL interoperability for real-time visualization Required Qualifications ... Experience with multi-threaded programming and async execution models * Experience with 3D point ...

Nvidia

High Performance AI Engineer

Austin, TX · On-site

We are looking for outstanding High Performance AI Engineer to build groundbreaking multi-agent systems for the CUDA ecosystem. We build innovative agentic runtimes and compiler-integrated ...

New

NVIDIA

Solutions Architect, Supercomputing

Austin, TX · On-site

$62.50 - $82.25/hr

Preferred : • Experience applying data science to industry problems. • Experience using GPGPU programming and design practices. • CUDA programming and optimization experience. • Background ...

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How much do cuda programmer jobs pay per hour?

As of Jul 16, 2026, the average hourly pay for cuda programmer in Texas is $36.83, according to ZipRecruiter salary data. Most workers in this role earn between $23.94 and $47.93 per hour, depending on experience, location, and employer.

What are the most common challenges faced by Cuda Programmers in their daily work?

Cuda Programmers often encounter challenges related to optimizing code performance and efficiently managing memory on GPU architectures. Debugging and profiling can be complex, as issues may arise from both the code and hardware-specific elements, requiring close attention to parallelization and bottlenecks. Collaboration is key, as you’ll typically work closely with software engineers, data scientists, or researchers to integrate and optimize code for specialized workflows. Successfully navigating these challenges helps drive significant performance improvements and innovation in high-performance computing applications.

What are the key skills and qualifications needed to thrive in the Cuda Programmer position, and why are they important?

To thrive as a Cuda Programmer, you need strong programming skills in C/C++ and parallel computing, with a solid understanding of GPU architectures and CUDA development. Familiarity with CUDA libraries, performance profiling tools, and platforms like NVIDIA Nsight or Visual Studio is often required, while certifications from NVIDIA can be advantageous. Problem-solving abilities, attention to detail, and effective teamwork and communication skills help set candidates apart. These competencies ensure you can optimize complex algorithms, work efficiently on high-performance computing projects, and collaborate smoothly with multidisciplinary teams.

What is a CUDA Programmer job?

A CUDA Programmer develops high-performance parallel computing applications using NVIDIA's CUDA (Compute Unified Device Architecture) framework. They optimize algorithms to run efficiently on GPUs, accelerating tasks such as machine learning, scientific simulations, and real-time data processing. This role requires proficiency in C/C++, an understanding of GPU architectures, and experience with parallel computing concepts to maximize performance.

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Cuda Programmer jobs near you
Infographic showing various Cuda Programmer job openings in Texas as of July 2026, with employment types broken down into 91% Full Time, 3% Part Time, 2% Temporary, 1% Contract, and 3% Nights. Highlights an 88% Physical, 4% Hybrid, and 8% Remote job distribution, with an average salary of $76,614 per year, or $36.8 per hour.
Senior HPC Storage Engineer

Senior HPC Storage Engineer

NVIDIA

Austin, TX • On-site

Full-time

Posted 25 days ago

Nvidia rating

9.3

Company rating: 9.3 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

15th of 209 rated software companies

Job description

Job Summary:
NVIDIA has been transforming computer graphics and accelerated computing for over 25 years, now focusing on AI to define the next era of computing. As a Senior HPC Storage Engineer, you will lead the research, design, and implementation of innovative storage solutions for high performance computing workloads, while collaborating with teams to optimize infrastructure performance and resource utilization.
Responsibilities:
• Research and analyze existing internal distributed storage services.
• Research, design, and implement scalable, next-gen distributed storage services for HPC workloads, optimizing both performance and cost-effectiveness to meet NVIDIA’s growing infrastructure needs
• Develop tooling to automate management of large-scale infrastructure environments, to automate operational monitoring and alerting, and to enable self-service consumption of resources.
• Detail the general procedures and practices, perform technology evaluations, related to distributed file systems.
• Collaborate across teams to better understand developers' workflows and capture their infrastructure requirements.
• Influence and guide methodologies for building, testing, and deploying applications to ensure efficient performance and resource utilization.
• Supporting our researchers to run their flows on our clusters including performance analysis and optimizations of deep learning workflows
• Root cause analysis and suggest corrective action for problems large and small scales
Qualifications:
Required:
• Bachelor’s degree in Computer Science, Electrical Engineering or related field or equivalent experience.
• 8+ years of experience designing and/or operating large scale storage infrastructure.
• Experience analyzing and tuning storage performance for a variety of workloads.
• Proficient in Centos/RHEL and/or Ubuntu Linux distros including Python programming and bash scripting
• In depth understanding of container technologies like Docker, Enroot
Preferred:
• Extensive experience with parallel and distributed filesystems (Ceph, Weka.io, Vast, Lustre, GPFS) and Linux storage kernel development.
• Proficient with NVIDIA GPUs, CUDA programming, and NCCL, including performance benchmarking via MLPerf.
• Deep familiarity with storage hardware (HDDs, SSDs, NVMe), enclosures, and specialized appliances like Network Appliance.
• Strong background in Software Defined Networking (SDN) and high-performance networking for AI/HPC clusters.
• Practical experience applying industry-standard frameworks, specifically PyTorch and TensorFlow.
Company:
NVIDIA is a computing platform company operating at the intersection of graphics, HPC, and AI. Founded in 1993, the company is headquartered in Santa Clara, USA, with a team of 10001+ employees. The company is currently Late Stage.

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Hours and flexibility

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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