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Cuda Engineer Jobs in Missouri (NOW HIRING)

Jobtailor

Solutions Architect, AI and ML

California, MO · On-site

$130 - $180/hr

Experience with TensorFlow or PyTorch, GPU, and CUDA * BS, MS, or PhD in Electrical/Computer Engineering, Computer Science, Statistics, Physics, or another Engineering field, or equivalent experience

Jobgether

$80K - $110K/yr

As a Senior Applied Research Engineer, you will help build the next generation of production-grade ... Strong understanding of CUDA and experience working within modern machine learning infrastructure.

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Cuda Engineer information

See Missouri salary details

$34.2K

$100.6K

$129K

How much do cuda engineer jobs pay per year?

As of Aug 30, 2026, the average yearly pay for cuda engineer in Missouri is $100,631.00, according to ZipRecruiter salary data. Most workers in this role earn between $83,000.00 and $127,600.00 per year, depending on experience, location, and employer.

What is a CUDA engineer?

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 are the key skills and qualifications needed to thrive as a CUDA engineer?

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 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 Missouri?

For Cuda Engineer jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Cuda Engineer jobs in Missouri look for?

The top searched job categories for Cuda Engineer jobs in Missouri are:

What cities in Missouri are hiring for Cuda Engineer jobs?

Cities in Missouri with the most Cuda Engineer job openings:

Cuda Engineer jobs near you
Infographic showing various Cuda Engineer job openings in Missouri as of August 2026, with employment types broken down into 94% Full Time, 3% Part Time, and 3% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution, with an average salary of $100,631 per year, or $48.4 per hour.

Software Engineer, CUDA Deep Learning Systems

California, MO • On-site

Jobtailor
See more jobs at Jobtailorexternal

$140 - $210/hr

Other

Posted 12 days ago

Job description

  • Explore, research, and prototype systems optimizations for advanced deep learning models at the intersection of high-level deep learning frameworks and low-level CUDA.
  • Architect and optimize distributed computing systems from single-node to cluster-scale supercomputing environments.
  • Design, implement, and optimize custom high-performance CUDA kernels for emerging neural network architectures and workloads.
  • Analyze hardware-software interactions to identify and resolve performance bottlenecks in training and inference pipelines.
  • Collaborate with AI researchers, hardware and software architects, kernel and compiler authors, and CUDA driver experts to co-design systems and algorithms.
  • Develop exploratory tools and runtime systems to profile and accelerate new deep learning paradigms.
  • Write clean, effective, and maintainable code and transition prototypes into open-source releases, framework integrations, internal tools, or commercial products.
Requirements
  • BS, MS, or PhD degree in Computer Science, Computer Engineering, Electrical Engineering, or related field, or equivalent experience.
  • 2+ years of relevant industry experience or equivalent academic experience after degree achievement.
  • Strong proficiency in C++ and Python programming.
  • Solid background in deep learning fundamentals, focused on transformers.
  • Strong understanding of distributed computing, multi-node scaling, and cluster-scale performance challenges.
  • Proven experience in systems programming, computer architecture, and low-level systems performance optimization.
  • Familiarity with GPU deep learning accelerator architectures.
  • Hands-on experience with CUDA programming, kernel optimization, and workload profiling.
  • Experience profiling and optimizing generative AI models, including large language models.
  • Research background in machine learning systems or adjacent fields.
  • Experience profiling and optimizing innovative vision models, generative AI architectures, or diffusion models.
  • Track record of initiative and willingness to deep-dive on problems across the stack.
  • Preferred experience with PyTorch, JAX, TensorRT, vLLM, sgLang, Nemo, or Megatron internals and execution graphs.
  • Preferred hands-on experience with NCCL, MPI, or UCX and distributed machine learning techniques.
  • Preferred knowledge of numerical methods and low-precision arithmetic such as NVFP4, MXFP4, FP8, and INT8.
  • Preferred background in deep learning compilers and ML systems, including Triton, XLA, and torch.compile.
  • Preferred experience with highly parallel or reinforcement-learning-style simulation environments.
  • Preferred experience designing and implementing agentic AI systems for complex systems and infrastructure problems.
Core Competencies

Demonstrates expertise in CUDA programming, high-performance computing, and deep learning optimization, with a strong foundation in distributed systems and machine learning architectures. Capable of collaborating with cross-functional teams to design and implement innovative solutions for advanced AI models.

Highest-signal resume keywords
  • CUDA Programming
  • C++ and Python Proficiency
  • Deep Learning Optimization
  • Distributed Computing Systems
  • Performance Bottleneck Analysis
ATS Optimization Keywords Hard Skills
  • CUDA
  • C++
  • Python
  • Deep Learning Fundamentals
  • Systems Programming
  • Computer Architecture
  • Kernel Optimization
  • Workload Profiling
  • Generative AI Models
  • Transformers
Soft Skills
  • Collaboration
  • Initiative
  • Problem-Solving
Industry Keywords
  • Machine Learning Systems
  • High-Performance Computing
  • AI Research
  • Neural Network Architectures
  • Cluster-Scale Performance
Tools & Technologies
  • PyTorch
  • JAX
  • TensorRT
  • NCCL
  • MPI
  • UCX
  • Triton
  • XLA
  • Torch.compile
  • SgLang
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