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Cuda Engineer Jobs in Spring, TX (NOW HIRING)

AI Architect, Manager

Houston, TX · On-site +1

$60.25 - $79.25/hr

... CUDA, ONNX, Caffe, etc. preferred), Computer Vision and other Azure Data and AI services. * Applying data and analytics to business use cases * Engineering background preferred in related areas ...

Physical and Spatial AI Architect

Houston, TX · On-site

$60.25 - $79.25/hr

Required : • Bachelor's degree in Engineering, Computer Science, Information Systems, Industrial ... drivers, CUDA container runtime, and cluster lifecycle automation • 2+ years automating ...

Excellent coding skills with one or more programming languages, such as C/C++, CUDA, FORTRAN, Python, OPENCL * Innovative mindset * Exceptional analytical and problem-solving skills * Highly ...

Excellent coding skills with one or more programming languages, such as C/C++, CUDA, FORTRAN, Python, OPENCL * Innovative mindset * Exceptional analytical and problem-solving skills * Highly ...

Excellent coding skills with one or more programming languages, such as C/C++, CUDA, FORTRAN, Python, OPENCL * Innovative mindset * Exceptional analytical and problem-solving skills * Highly ...

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Showing results 1-20

Cuda Engineer information

See Spring, TX salary details

$32.5K

$95.5K

$122.4K

How much do cuda engineer jobs pay per year?

As of May 29, 2026, the average yearly pay for cuda engineer in Spring, TX is $95,469.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,800.00 and $121,000.00 per year, depending on experience, location, and employer.

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.

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 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 popular job titles related to Cuda Engineer jobs in Spring, TX? For Cuda Engineer jobs in Spring, TX, the most frequently searched job titles are:
What job categories do people searching Cuda Engineer jobs in Spring, TX look for? The top searched job categories for Cuda Engineer jobs in Spring, TX are:
What cities near Spring, TX are hiring for Cuda Engineer jobs? Cities near Spring, TX with the most Cuda Engineer job openings:
HPC AI Solution Architect (S2S)

HPC AI Solution Architect (S2S)

Deloitte

Houston, TX

Other

Posted 8 days ago


Deloitte rating

8.1

Company rating: 8.1 out of 10

Based on 86 frontline employees who took The Breakroom Quiz

59th of 138 rated financial services


Job description

Lead Cloud HPC- AI Infrastructure Architect(S2S)

As a Lead Cloud Integrated Infra Engineer on the Silicon2Service team in Deloitte's AI & Engineering practice, you will design and drive deployment of fully integrated architectures for GPU-accelerated AI factories and high-performance computing infrastructure in close partnership with Deloitte AI specialists and our ecosystem partners. You will shape end-to-end solutions-from discovery and reference architecture mapping through sizing and implementation.  You will partner with Sales Executives, AI application specialists, delivery engineering, and managed services to help clients achieve measurable outcomes from private AI assets. You will lead technical solution strategy for pursuits and active opportunities and translate complex client needs into clear, complete solutions and delivery requirements.

Recruiting for this role ends on 6/26/2026.


Work you'll do
As a Lead Cloud Integrated Infra Engineer on the Silicon2Service team, you will be responsible for:

  • Leading architecture for pursuits and active opportunities, including discovery, requirements, constraints, and target-state design
  • Creatively defining reference architectures for on-premises, cloud, and hybrid GPU platforms across compute, network, storage, security, software and operations
  • Driving architecture trade-offs and decisions across performance, scalability, reliability, locality, total cost of ownership, time-to-value, and risk
  • Owning the technical solution strategy in proposals and RFPs, including architecture narrative, assumptions, dependencies, sizing guidance, and delivery approach
  • Facilitating client workshops and technical reviews and translating engineering detail into executive-ready communications
  • Architecting complex, innovative technology solutions with a focus on business outcomes, cost of quality, and long-term scalability and sustainability.
  • Engaging with C-Suite client leadership during sales and delivery, including leading technical pre-sales discussions, shaping proposals, and supporting the closing of new business opportunities
  •  Supporting go-to-market strategies, including participation in industry events, conferences, and client briefings

The Team

The Silicon to Service team at Deloitte delivers end-to-end AI factories and advanced technology services that help organizations build, deploy, and operate large-scale, private AI and data platforms. We enable the next phase of enterprise AI adoption through private AI economics with cloud-like ese of use.  Join this unique opportunity to work on innovative AI platforms and emerging technologies in the rapidly evolving AI market while solving complex enterprise problems for some of the world's largest organizations.


Qualifications

Required:

  • 10+ years of experience in infrastructure architecture or engineering for large-scale platforms including design, implementation, operations, and optimization.
  • 4+ years designing or delivering GPU-accelerated platforms for AI, ML, or high-performance computing
  • 3+ years Linux system administration in production environments
  • 3+ years designing or operating distributed compute clusters for AI/HPC in hybrid cloud setups, including multi-GPU topologies, partitioning, scheduler integration, and scalability for edge-to-cloud workloads.
  • 2+ years with high-performance networking or storage for AI/HPC
  • 2+ years building containerized platforms using Kubernetes or Red Hat OpenShift, including GPU operators/drivers, CUDA container runtime, and cluster lifecycle automation
  • 2+ years automating infrastructure as code(IaC) with tools like Terraform and Ansible
  • At least 2 end-to-end deployments of reference architectures in the cloud or on-prem, including variants with security controls, network segmentation, operational runbooks, and validation testing
  • Experience in pre-sales or sales engineering, including discovery, solution demonstrations, and proposal/RFP contributions
  • Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve.
  • Limited immigration sponsorship may be available.

Preferred:

  • 2+ years implementing AI/HPC cluster scheduling  (Slurm and Kubernetes), including multi-tenant queues, quotas, and GPU-aware policies
  • 2+ years supporting generative AI infrastructure patterns, including multi-node distributed training
  • Experience with AI agents and frameworks
  • Experience with high-throughput storage for AI/HPC
  • Experience executing NVIDIA co-sell motions with OEMS (Dell, HPC, Lenovo), CSPs ( AWS, Azure, Google Cloud), or independent software vendors ( Run:ai, OpenShift, Weights & Biases)

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $141,200 to $278,300.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

Qualifications:

Lead Cloud HPC- AI Infrastructure Architect(S2S)

As a Lead Cloud Integrated Infra Engineer on the Silicon2Service team in Deloitte's AI & Engineering practice, you will design and drive deployment of fully integrated architectures for GPU-accelerated AI factories and high-performance computing infrastructure in close partnership with Deloitte AI specialists and our ecosystem partners. You will shape end-to-end solutions-from discovery and reference architecture mapping through sizing and implementation.  You will partner with Sales Executives, AI application specialists, delivery engineering, and managed services to help clients achieve measurable outcomes from private AI assets. You will lead technical solution strategy for pursuits and active opportunities and translate complex client needs into clear, complete solutions and delivery requirements.

Recruiting for this role ends on 6/26/2026.


Work you'll do
As a Lead Cloud Integrated Infra Engineer on the Silicon2Service team, you will be responsible for:

  • Leading architecture for pursuits and active opportunities, including discovery, requirements, constraints, and target-state design
  • Creatively defining reference architectures for on-premises, cloud, and hybrid GPU platforms across compute, network, storage, security, software and operations
  • Driving architecture trade-offs and decisions across performance, scalability, reliability, locality, total cost of ownership, time-to-value, and risk
  • Owning the technical solution strategy in proposals and RFPs, including architecture narrative, assumptions, dependencies, sizing guidance, and delivery approach
  • Facilitating client workshops and technical reviews and translating engineering detail into executive-ready communications
  • Architecting complex, innovative technology solutions with a focus on business outcomes, cost of quality, and long-term scalability and sustainability.
  • Engaging with C-Suite client leadership during sales and delivery, including leading technical pre-sales discussions, shaping proposals, and supporting the closing of new business opportunities
  •  Supporting go-to-market strategies, including participation in industry events, conferences, and client briefings

The Team

The Silicon to Service team at Deloitte delivers end-to-end AI factories and advanced technology services that help organizations build, deploy, and operate large-scale, private AI and data platforms. We enable the next phase of enterprise AI adoption through private AI economics with cloud-like ese of use.  Join this unique opportunity to work on innovative AI platforms and emerging technologies in the rapidly evolving AI market while solving complex enterprise problems for some of the world's largest organizations.


Qualifications

Required:

  • 10+ years of experience in infrastructure architecture or engineering for large-scale platforms including design, implementation, operations, and optimization.
  • 4+ years designing or delivering GPU-accelerated platforms for AI, ML, or high-performance computing
  • 3+ years Linux system administration in production environments
  • 3+ years designing or operating distributed compute clusters for AI/HPC in hybrid cloud setups, including multi-GPU topologies, partitioning, scheduler integration, and scalability for edge-to-cloud workloads.
  • 2+ years with high-performance networking or storage for AI/HPC
  • 2+ years building containerized platforms using Kubernetes or Red Hat OpenShift, including GPU operators/drivers, CUDA container runtime, and cluster lifecycle automation
  • 2+ years automating infrastructure as code(IaC) with tools like Terraform and Ansible
  • At least 2 end-to-end deployments of reference architectures in the cloud or on-prem, including variants with security controls, network segmentation, operational runbooks, and validation testing
  • Experience in pre-sales or sales engineering, including discovery, solution demonstrations, and proposal/RFP contributions
  • Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve.
  • Limited immigration sponsorship may be available.

Preferred:

  • 2+ years implementing AI/HPC cluster scheduling  (Slurm and Kubernetes), including multi-tenant queues, quotas, and GPU-aware policies
  • 2+ years supporting generative AI infrastructure patterns, including multi-node distributed training
  • Experience with AI agents and frameworks
  • Experience with high-throughput storage for AI/HPC
  • Experience executing NVIDIA co-sell motions with OEMS (Dell, HPC, Lenovo), CSPs ( AWS, Azure, Google Cloud), or independent software vendors ( Run:ai, OpenShift, Weights & Biases)

The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $141,200 to $278,300.

You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.

Education:Bachelor's DegreeEmployment Type:

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