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Gpu Computing Jobs in Missouri (NOW HIRING)

Accenture

AI & HPC Infrastructure Engineer

Saint Louis, MO · On-site

$97K - $127K/yr

... GPU-accelerated workloads, large-scale models, simulations, and emerging agentic AI solutions at ... Design and implement AI infrastructure and accelerated computing solutions, aligning system ...

Jobtailor

Senior ASIC Design Engineer

California, MO · On-site

$168 - $310.50/hr

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 ...

Nebius

Data Center IT Manager

Kansas City, MO

$146K - $220K/yr

... computing (HPC), and hyperscale cloud services . Our data centers host large-scale GPU clusters purpose-built for AI training, inference, and accelerated workloads. As global data center operations ...

Walmart

Distinguished, Software Engineer

Noel, MO · On-site

$130K - $260K/yr

Our platform combines secure sandboxed execution, micro-VM technology, serverless computing ... Deep expertise with Kubernetes, containers, serverless, GPU, statefulset, CICD, git ops ...

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Gpu Computing information

What is GPU computing?

GPU computing refers to the use of a Graphics Processing Unit (GPU) alongside a Central Processing Unit (CPU) to accelerate computational tasks. GPUs are highly efficient at performing parallel operations, making them ideal for complex calculations in fields like machine learning, scientific simulations, and graphics rendering. Unlike traditional CPUs, GPUs can process thousands of threads simultaneously, greatly speeding up tasks that involve large-scale data processing. This makes GPU computing essential in industries requiring high-performance computing solutions.

What are some common challenges faced by GPU computing professionals when optimizing code for parallel processing?

One of the main challenges in GPU Computing is efficiently restructuring code to leverage the massive parallelism that GPUs offer. Professionals often encounter issues with memory management, synchronization between threads, and minimizing data transfer between CPU and GPU to avoid bottlenecks. Additionally, debugging parallel code can be complex, as errors may not manifest consistently across runs. Collaborating with software engineers, data scientists, and hardware specialists is typical to ensure optimal performance and scalability in real-world applications.

What are the key skills and qualifications needed to thrive as a GPU computing specialist, and why are they important?

To thrive as a GPU Computing Specialist, you need expertise in parallel programming, computer architecture, and a strong foundation in mathematics and algorithms, often supported by a degree in computer science, engineering, or related fields. Familiarity with programming languages like C/C++, CUDA, OpenCL, and experience with GPU hardware and high-performance computing systems are essential. Problem-solving abilities, analytical thinking, and strong collaboration skills help you innovate and work effectively on complex computational projects. These skills ensure efficient development, optimization, and deployment of GPU-accelerated solutions crucial for scientific, engineering, and AI applications.

What is the difference between Gpu Computing vs Data Scientist?

AspectGpu ComputingData Scientist
Required CredentialsKnowledge of GPU architectures, programming skills in CUDA or OpenCLDegree in Computer Science, Statistics, or related fields; strong programming skills
Work EnvironmentHigh-performance computing environments, data centers, research labsOffice settings, research institutions, tech companies
Industry UsageMachine learning, scientific simulations, graphics renderingData analysis, predictive modeling, business insights

Gpu Computing focuses on leveraging GPU hardware for high-speed processing tasks, often requiring specialized programming skills. Data Scientists analyze data to extract insights, using various tools and statistical methods. While both roles involve data and computing, Gpu Computing is more hardware and performance-oriented, whereas Data Scientists focus on data analysis and modeling.

What are popular job titles related to Gpu Computing jobs in Missouri?

For Gpu Computing jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Gpu Computing jobs in Missouri look for?

The top searched job categories for Gpu Computing jobs in Missouri are:

Gpu Computing jobs near you
Infographic showing various Gpu Computing job openings in Missouri as of July 2026, with employment types broken down into 79% Full Time, 18% Part Time, and 3% Contract. Highlights an 77% Physical, 4% Hybrid, and 19% Remote job distribution.

Senior ASIC Design Efficiency Engineer

Jobtailor

California, MO • On-site

$180 - $300/hr

Other

Posted 5 days ago

Job description

  • Develop innovative HW, GPU and system designs to extend the state of the art performance and efficiency.
  • Understand the design and implementation, develop methodology and infrastructure to drive Performance, Power and Area (PPA) improvements.
  • Execute and deliver fully verified, high performance, area and power efficient RTL to achieve design targets.
  • Collaborate with architects, designers, verification and VLSI teams to craft the industries' top performing GPUs.
Requirements
  • Bachelors Degree in EE or CE or equivalent experience.
  • 12+ years of experience.
  • Proficiency in SystemVerilog or similar HDL.
  • Solid understanding of logic design and computer architecture.
  • Strong interpersonal skills are required along with the ability to work in a diverse product oriented team.
Core Competencies

Demonstrates expertise in developing high-performance hardware and GPU designs, with a strong focus on Performance, Power, and Area (PPA) improvements. Proficient in SystemVerilog and logic design, with extensive experience collaborating in diverse, product-oriented teams.

Highest-signal resume keywords
  • SystemVerilog Proficiency
  • Logic Design Understanding
  • Computer Architecture Knowledge
  • Performance, Power, Area (PPA) Improvements
  • 12+ Years Experience
ATS Optimization Keywords Hard Skills
  • SystemVerilog
  • HDL
  • Logic Design
  • Computer Architecture
  • RTL Design
  • Performance Optimization
  • Power Efficiency
  • Area Efficiency
  • Verification Methodology
  • Infrastructure Development
Soft Skills
  • Interpersonal Skills
  • Team Collaboration
Certifications & Qualifications
  • Bachelors Degree in Electrical Engineering
  • Bachelors Degree in Computer Engineering
Industry Keywords
  • GPU Design
  • VLSI
  • High Performance Computing
  • Design Verification
  • Product Oriented Team
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