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Gpu Jobs in Colorado (NOW HIRING)

Introl

Data Center Technician

Denver, CO · On-site

$30 - $36/hr

The city attracts enterprises seeking GPU computing resources for AI and analytics applications. About Introl Introl stands apart as a leader in GPU infrastructure deployments, specializing in large ...

AppFolio

Staff Machine Learning Engineer

Denver, CO · On-site

$200 - $250/hr

Design and operate AppFolio's ML infrastructure on AWS, including ECS, SageMaker, GPU fleets, model serving, autoscaling, and cost controls. * Optimize AI cost across all applications through routing ...

In Tandem

AI Engineer

Denver, CO · On-site +1

$100K - $135K/yr

Run the inference serving layer on our own GPU hardware: choose and tune the serving stack (vLLM, SGLang, TensorRT-LLM) for high throughput and low latency. * Optimize aggressively: tensor ...

Nou Systems Inc

DevOps Engineer

Colorado Springs, CO · On-site

$120K - $165K/yr

Build and maintain GPU-capable development environments and containerized workflows. * Develop CI/CD pipelines (e.g., GitLab CI) with automated testing, linting, and deployment. * Implement and ...

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How much do gpu jobs pay per hour?

As of Aug 9, 2026, the average hourly pay for gpu in Colorado is $57.77, according to ZipRecruiter salary data. Most workers in this role earn between $56.88 and $68.27 per hour, depending on experience, location, and employer.

What is the difference between Gpu vs Data Scientist?

AspectGpuData Scientist
Required CredentialsKnowledge of parallel computing, programming skills (CUDA, OpenCL)Degree in Computer Science, Statistics, or related fields; programming skills
Work EnvironmentHardware-focused, technical, often in R&D or engineering teamsData analysis, modeling, research in various industries
Industry UsageTech, gaming, AI, machine learningFinance, healthcare, tech, marketing

Gpu specialists focus on hardware and parallel processing for computing tasks, while data scientists analyze data to extract insights. Both roles require technical skills, but Gpu roles are more hardware-oriented, whereas data scientists focus on data analysis and modeling.

What is a GPU engineer?

A GPU job refers to a computing task that utilizes a Graphics Processing Unit (GPU) for acceleration. GPUs are specialized processors designed for parallel processing, making them ideal for tasks like machine learning, scientific simulations, and rendering. Many software applications offload intensive computations to GPUs to improve performance and efficiency. Jobs related to GPUs can involve programming, optimization, and hardware configuration in fields like AI, gaming, and data analysis.

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

To thrive as a GPU Engineer, you need a solid background in computer engineering, mathematics, and programming languages such as C++ or CUDA, often supported by a relevant degree. Familiarity with GPU architectures, parallel computing frameworks, and tools like OpenCL or Vulkan is typically required. Analytical thinking, problem-solving, and teamwork are essential soft skills for innovating and debugging complex systems. These abilities are crucial for optimizing performance, ensuring compatibility, and driving advancements in graphics and computational workloads.

What is a GPU?

A GPU, or Graphics Processing Unit, is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images and graphics for display. While originally developed for rendering graphics in video games and visual applications, GPUs are now widely used for parallel processing tasks in areas such as artificial intelligence, data science, and scientific computing. Their architecture allows them to handle thousands of operations simultaneously, making them much faster than traditional CPUs for certain workloads.

What are some common challenges faced by GPU engineers when optimizing performance for various applications?

GPU engineers often encounter challenges such as balancing high computational throughput with power efficiency, ensuring compatibility across different hardware architectures, and optimizing code for parallel processing. They must also troubleshoot bottlenecks in memory bandwidth and latency that can impact performance. Collaboration with software developers and hardware architects is crucial to identify and resolve these issues, and staying updated with the latest advances in GPU technologies is essential for continued success.
What are the most commonly searched types of Gpu jobs in Colorado? The most popular types of Gpu jobs in Colorado are:
What are popular job titles related to Gpu jobs in Colorado? For Gpu jobs in Colorado, the most frequently searched job titles are:
What job categories do people searching Gpu jobs in Colorado look for? The top searched job categories for Gpu jobs in Colorado are:
What cities in Colorado are hiring for Gpu jobs? Cities in Colorado with the most Gpu job openings:
Gpu jobs near you
Infographic showing various Gpu job openings in Colorado as of August 2026, with employment types broken down into 91% Full Time, 6% Part Time, and 3% Temporary. Highlights an 85% In-person, 3% Hybrid, and 12% Remote job distribution, with an average salary of $120,168 per year, or $57.8 per hour.

Staff Engineer, Inference Optimizations

DigitalOcean

Denver, CO • Remote

$191K - $239K/yr

Full-time

Posted 17 days ago

Job description

DigitalOcean is seeking a Senior Engineer 2 to play a key technical role in our AI Inference Optimization team. DigitalOcean aims to be the Inference Cloud of choice for digitally native companies and you will help ensure we can offer the industry-leading performance for our inference services. You will be responsible for the architectural decisions that maximize throughput and minimize latency for the world's most advanced large models. As an IC leader, you will act as a force multiplier for the engineering organization, solving the most complex bottlenecks in memory bandwidth and compute utilization while guiding the technical roadmap for our high-performance inference fleet.

What You'll Do:
  • Performance Architecture: Lead the technical strategy for benchmarking and performance optimizations at the inference engine and GPU kernel layers, ensuring our infrastructure extracts maximum value from every TFLOP.
  • Deep-Dive Optimization: Engineer solutions for complex performance issues, including attention layer optimizations, memory and precision management, and advanced parallelization across multi-node GPU clusters. 
  • Technological Innovation: Proactively implement cutting-edge optimization techniques to keep DigitalOcean at the forefront of the Gen AI landscape. Some examples of projects you may work on:
    • Improving batch size performance using AMD's AITER library for AMD MI355X - identify and tune AITER's CK (composable kernel) or ASK (assembly) to optimize FP8 / BF16 
    • Identify kernel fusion opportunities for GLM-5 kernels for different layers of the Transformer block (FlashAttention, RMS Norm)
    • Tune expert gateway router kernels for MoE models like Qwen3-235B, DeepSeek V3, GLM-5 etc
  • Hardware & Ecosystem Mastery: Act as the subject matter expert on modern GPU families (NVIDIA/AMD) and their software stacks (CUDA, ROCm, TensorRT, OpenAI Triton), advising on hardware procurement and software integration.
  • Precision Optimization: Develop and deploy state-of-the-art quantization techniques (FP8, INT8, and experimental FP4) to double throughput without losing accuracy.
  • Technical Mentorship: Lead by example through high-quality code and design reviews, elevating the technical bar for the team without the administrative overhead of direct management.
  • Strategic Collaboration: Partner with Product Management and TPMs to translate "theoretical hardware limits" into "shippable product features," ensuring our platform is both powerful and developer-friendly.
  • Community Leadership: Maintain a strong presence in the GPU infrastructure and model performance optimization communities, contributing to and integrating the best of open-source AI.
What You'll Bring to DigitalOcean:
  • Technical Depth: 5+ years of experience in high-performance computing or AI infrastructure, with a proven track record of solving compute utilization and memory bandwidth bottlenecks.
  • Gen AI Literacy: Deep familiarity with the Gen AI (LLM, VLM, LMM) landscape, including the specific quirks and architectural requirements of major model families.
  • Optimization Expert: Hands-on experience with attention-layer optimizations and parallelization strategies across distributed GPU environments.
  • Hardware Fluency: Comprehensive understanding of NVIDIA and AMD GPU architectures and their respective software ecosystems (CUDA, ROCm, etc.).
  • Open Source Mastery: Extensive experience integrating, building with, and contributing to open-source software projects.
  • Systems Design: Excellent system design skills, particularly related to low-level GPU programming - optimization, memory access patterns, and parallel execution.
  • Leadership through Influence: Experience acting as a technical lead, driving design and delivery through cross-functional alignment and expert-level delegation.
  • Low-Level Mastery: Deep understanding of GPU architectures (SMs, Warp scheduling, Tensor Cores).
  • The Toolkit: Expert-level Triton or CUDA. If you've contributed to the Triton compiler or wrote custom CUDA kernels for a major LLM, we want you.
Compensation Range: 
  • $191,200 - $239,000

*This is a remote role

JR: 2026-7625

#LI-Remote

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