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Assistant Cuda Jobs in Colorado (NOW HIRING)

Computer Vision AI Engineer

Aurora, CO · On-site

$99K - $225K/yr

Utilize GPU programming, including CUDA or RAPIDs, to optimize the performance of computer vision ... As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with ...

Computer Vision AI Engineer

Aurora, CO · On-site

$99K - $225K/yr

Utilize GPU programming, including CUDA or RAPIDs, to optimize the performance of computer vision ... As part of this commitment, the use of artificial intelligence (AI) or other tools to assist with ...

Assistant Cuda information

What are the roles and responsibilities of an assistant CUDA developer?

An Assistant Cuda typically supports senior CUDA (Compute Unified Device Architecture) developers or teams working with NVIDIA’s parallel computing platform. Their responsibilities include assisting in developing, testing, and optimizing code written for GPUs to accelerate computing tasks, debugging CUDA applications, and maintaining documentation. They may also handle routine tasks such as performance benchmarking, code reviews, and collaborating with other team members to implement efficient GPU solutions. This role is crucial in organizations that rely on high-performance computing, scientific simulations, or AI workloads.

What skills and qualifications are needed to thrive as an assistant CUDA developer?

To thrive as an Assistant CUDA Developer, you need strong programming skills in C/C++, a solid understanding of parallel computing concepts, and familiarity with GPU architectures, often backed by a degree in computer science or a related field. Proficiency with CUDA development tools, debugging utilities, and version control systems like Git is typically required. Attention to detail, problem-solving abilities, and effective communication are crucial soft skills for collaborating with teams and optimizing code. These skills ensure efficient development of high-performance applications and successful integration of GPU acceleration into software solutions.

What are common challenges faced by assistant CUDA developers when optimizing code for GPU performance?

Assistant CUDA developers often encounter challenges such as managing memory efficiently between the host and device, ensuring proper kernel parallelization, and avoiding thread divergence. Balancing occupancy and resource usage can also be tricky, as it requires a deep understanding of how CUDA schedules and executes threads. Collaborating closely with data scientists and other engineers is essential to identify performance bottlenecks and implement effective optimizations.

What is the difference between Assistant Cuda vs Assistant Data Analyst?

AspectAssistant CudaAssistant Data Analyst
Required CredentialsTypically a relevant degree in computer science or related fieldOften a degree in data science, statistics, or related field
Work EnvironmentTech companies, software development teams, AI projectsBusiness, finance, marketing, or research departments
Employer & Industry UsageUsed in tech and AI industries for supporting CUDA programming tasksCommon in data-driven industries for data processing and analysis

Assistant Cuda and Assistant Data Analyst roles share some technical background but differ mainly in focus. Assistant Cuda primarily supports GPU programming and AI development, while Assistant Data Analyst focuses on data interpretation and reporting. Both roles require relevant technical skills and are found in industries leveraging data and technology, but their daily tasks and industry applications vary significantly.

What are the most commonly searched types of Cuda jobs in Colorado?

The most popular types of Cuda jobs in Colorado are:

What are popular job titles related to Assistant Cuda jobs in Colorado?

For Assistant Cuda jobs in Colorado, the most frequently searched job titles are:

What cities in Colorado are hiring for Assistant Cuda jobs?

Cities in Colorado with the most Assistant Cuda job openings:

Infographic showing various Assistant Cuda job openings in Colorado as of June 2026, with employment types broken down into 1% As Needed, 95% Full Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 4% Hybrid, and 8% Remote job distribution.

Senior Embedded Linux Software Platform Engineer - ROS2 Robotics

AION ROBOTICS CORPORATION

Arvada, CO • On-site

$127K - $166K/yr

Full-time

Re-posted 22 days ago


Job description

Job Summary:
AION Robotics Corporation is a rapidly growing startup manufacturing advanced, rugged autonomous ground vehicles. They are seeking a highly skilled Senior Embedded Linux Software Platform Engineer to manage large-scale cross-compiled codebases, container environments, and Linux image deployment for NVIDIA Jetson-based edge devices.
Responsibilities:
• Own and maintain CMake-based build systems for large-scale, modular, cross-compiled codebases targeting heterogeneous platforms (x86, ARM, CUDA/Jetson).
• Lead development of efficient, reproducible build pipelines using modern toolchains and best practices for multi-target deployment.Provide internal consulting on complex build problems, dependency resolution, and build caching techniques.
• Design and maintain highly optimized Docker container images tailored for embedded and ROS2 environments, with a focus on layering, performance, and security.
• Create and manage internal system-level packages and local repositories to support in-house software distribution.
• Build and maintain containerized runtime environments compatible with NVIDIA Jetson and CUDA acceleration.
• Architect, implement, and maintain CI/CD pipelines in CircleCI or similar platforms for automated building, testing, and deployment of embedded software stacks.
• Integrate image creation and container publishing into the CI/CD pipeline for seamless field updates and delivery.
• Build and customize embedded Linux system images for NVIDIA Jetson platforms (L4T/Jetpack), including kernel module integration, device tree overlays, and systemd.
• Configure and tune process/network system performance parameters for real-time and safety-critical applications.
• Maintain scripts and infrastructure for reliable, reproducible system image builds.
• Apply best practices for ROS2 integration in cross-compiled and containerized environments.
• Support efficient unpacking and deployment of Jetson/CUDA/ROS2 packages into system images and containers.
• Assist with integration of third-party drivers and SDKs (e.g., ZED camera, CUDA-based libraries) into Jetson kernel/BSP.
• Conduct in-depth code reviews with a strong eye for low-level and high-level issues including memory safety, C++ best practices (Rule of 3/5/0), and resource management.
• Advocate for clean, maintainable code with high reliability and strong reproducibility across platforms.
• Mentor other engineers on system-level concerns and advanced CMake practices.
Qualifications:
Required:
• 5+ years of experience with build systems, especially expert-level with CMake in large-scale projects.
• Strong understanding of cross-compilation workflows targeting ARM and CUDA/NVIDIA Jetson platforms.
• Expertise in Docker container creation, optimization, and secure image management.
• Solid background in building and configuring Linux system images (e.g., using Jetpack SDK, L4T, Yocto or similar).
• Experience writing, debugging, and maintaining CI/CD pipelines, preferably in CircleCI, GitHub Actions, or GitLab CI.
• Proficient with system-level Linux administration, including kernel configuration, systemd, networking, and real-time tuning.
• Experience with ROS2 middleware, including build and packaging strategies in embedded or containerized environments.
• Demonstrated ability to troubleshoot and resolve deep integration issues (e.g., kernel drivers, bootloaders, BSP customization).
Preferred:
• Experience with real-time and safety-critical Linux systems (PREEMPT_RT, CPU pinning, cgroups, etc.).
• Familiarity with camera drivers, BSP integration (e.g., ZED SDK), or other complex peripheral integration workflows.
• Ability to reverse-engineer or replicate undocumented integration efforts for device-specific packaging.
• Passion for code quality, linting, static analysis, and meticulous review processes.
Company:
Our rugged autonomous vehicles bring Industry 4.0 to outdoor commercial jobsites through the automation of infrastructure monitoring, maintenance and inspection tasks. Founded in 2016, the company is headquartered in Denver, USA, with a team of 11-50 employees. The company is currently Early Stage.