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Gpu Entry Level Jobs (NOW HIRING)

XPENG

AI Intern - VLA Deployment

Santa Clara, CA · On-site

We are looking for an entry-level engineer or intern to support the optimization and deployment of ... Experience with CUDA or GPU programming. * Exposure to autonomous driving, robotics, or real-time ...

Eridu AI

Mechanical Engineer Consultant

Saratoga, CA · On-site

Eridu introduces multiple industry-first innovations across silicon, packaging, software, and systems to deliver an order of magnitude improvement in performance and unlock greater GPU utilization to ...

Hut 8

Operations Coordinator - Highrise.ai

Miami, FL

This is an entry-level role designed for a young generalist who wants to enter a fast- moving AI ... Genuinely curious about AI, GPU infrastructure, and emerging technology * Comfortable in a fast ...

Aurora Innovation

Software Engineer I

Mountain View, CA · On-site

$116K - $174K/yr

Profile and optimize code to ensure efficient use of limited CPU, GPU, and memory resources on the ... LI-Entry-Level Working at Aurora At Aurora, we bring together extraordinarily talented and ...

Hut 8

Operations Coordinator - Highrise.ai

Miami, FL · On-site

This is an entry-level role designed for a young generalist who wants to enter a fast- moving AI ... Genuinely curious about AI, GPU infrastructure, and emerging technology * Comfortable in a fast ...

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All JobsGpu Entry Level Jobs

Gpu Entry Level information

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$25K

$47.8K

$68.5K

How much do gpu entry level jobs pay per year?

As of May 31, 2026, the average yearly pay for gpu entry level in the United States is $47,831.00, according to ZipRecruiter salary data. Most workers in this role earn between $40,500.00 and $52,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an Entry-Level GPU Engineer, and why are they important?

To thrive as an Entry-Level GPU Engineer, you typically need a solid background in computer science or electrical engineering, with knowledge of graphics programming languages such as CUDA or OpenCL. Familiarity with GPU architectures, hardware debugging tools, and version control systems like Git is often required. Strong problem-solving skills, attention to detail, and the ability to work collaboratively are important soft skills in this role. These competencies ensure effective development and optimization of GPU solutions, contributing to the performance and reliability of graphics and computing applications.

What are some typical challenges faced by entry-level GPU engineers, and how can they overcome them?

Entry-level GPU engineers often encounter challenges such as getting familiar with complex hardware architectures, optimizing code for parallel processing, and debugging performance issues. To overcome these, it's helpful to actively seek mentorship, participate in code reviews, and use profiling tools to analyze performance bottlenecks. Collaborating with more experienced team members and staying updated with the latest GPU development practices can significantly ease the learning curve and foster professional growth.

What are GPU entry-level jobs?

GPU entry-level jobs are positions designed for individuals who are new to working with graphics processing units (GPUs) and related technologies. These roles typically involve assisting in the development, testing, or optimization of GPU hardware or software, and may include tasks such as programming, debugging, or supporting graphics applications. Candidates often have a background in computer science, electrical engineering, or a related field, but may not require extensive professional experience. Entry-level GPU jobs are a great way to gain hands-on experience and build skills in areas like parallel computing, 3D graphics, and machine learning using GPUs.

What is the difference between Gpu Entry Level vs Gpu Technician?

AspectGpu Entry LevelGpu Technician
Required CredentialsHigh school diploma or equivalent; basic understanding of GPU hardwareAssociate degree or certification in computer hardware or electronics; technical training
Work EnvironmentEntry-level positions, often in retail or support rolesTechnical repair shops, data centers, or manufacturing facilities
Industry UsageCustomer support, basic troubleshootingHardware diagnostics, repairs, and maintenance

The main difference is that Gpu Entry Level roles focus on basic support and customer service, while Gpu Technicians perform hands-on hardware repairs and diagnostics. Gpu Technicians typically require more technical training and certifications, working in specialized environments. Both roles are essential in the GPU industry but differ in complexity and responsibilities.

More about Gpu Entry Level jobs
What cities are hiring for Gpu Entry Level jobs? Cities with the most Gpu Entry Level job openings:
What are the most commonly searched types of Gpu jobs? The most popular types of Gpu jobs are:
What states have the most Gpu Entry Level jobs? States with the most job openings for Gpu Entry Level jobs include:
What job categories do people searching Gpu Entry Level jobs look for? The top searched job categories for Gpu Entry Level jobs are:
Gpu Entry Level jobs near you
Infographic showing various Gpu Entry Level job openings in the United States as of May 2026, with employment types broken down into 1% Internship, 82% Full Time, 14% Part Time, 2% Contract, and 1% Nights. Highlights an 97% Physical, 1% Hybrid, and 2% Remote job distribution, with an average salary of $47,831 per year, or $23 per hour.

AI Intern - VLA Deployment

XPENG

Santa Clara, CA • On-site

Internship

Posted 26 days ago

Job description

XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity.
The Mission: Vision-Language-Action (VLA) models and foundation models are becoming increasingly important in autonomous driving, but turning research models into real-time, production-ready systems on vehicle hardware remains a major challenge. We are looking for an entry-level engineer or intern to support the optimization and deployment of multimodal models onto vehicle-grade compute platforms. This role is a strong fit for candidates who are excited about deep learning systems, model deployment, and edge inference for real-world autonomous driving applications.
Key Responsibilities
  • Support model quantization and deployment efforts for large-scale multimodal models, including Transformers and vision-language models.
  • Assist with applying model optimization techniques such as post-training quantization, quantization-aware training, pruning, and related compression methods under guidance from senior engineers.
  • Work with research and platform teams to help improve model deployability and understand hardware and runtime constraints.
  • Contribute to deployment tools, test pipelines, and runtime modules in C++ and Python for autonomous driving systems.
  • Help analyze model performance, memory usage, latency, and numerical accuracy across different deployment targets.
  • Participate in debugging and performance tuning across the model, runtime, and system stack.
  • Support validation and testing workflows to ensure stable and reliable deployment in vehicle and simulation environments.

Basic Qualifications
  • BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics, or a related field.
  • Strong programming skills in C++ and/or Python.
  • Familiarity with deep learning frameworks such as PyTorch.
  • Basic understanding of model inference, deployment, or optimization workflows using tools such as ONNX, TensorRT, or similar frameworks.
  • Exposure to model compression or quantization concepts such as INT8, FP16, or related approaches.
  • Interest in computer architecture, performance optimization, and edge or embedded systems.
  • Strong problem-solving skills and the ability to learn quickly in a fast-paced engineering environment.
  • Good communication skills and the ability to collaborate with cross-functional teams.

Preferred Qualifications
  • Internship, research, or project experience in deep learning model deployment, inference acceleration, or embedded AI.
  • Familiarity with Transformers, multimodal models, or foundation models.
  • Experience with CUDA or GPU programming.
  • Exposure to autonomous driving, robotics, or real-time systems.
  • Contributions to research projects, open-source repositories, or relevant course projects.

What do we provide:
  • A fun, supportive and engaging environment.
  • Infrastructures and computational resources to support your work.
  • Opportunity to work on cutting edge technologies with the top talents in the field.
  • Opportunity to make significant impact on the transportation revolution by the means of advancing autonomous driving.
  • Competitive compensation package.
  • Snacks, lunches, dinners, and fun activities.

We are an Equal Opportunity Employer. It is our policy to provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other prescribed category set forth in federal or state regulations.

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