Research Scientist Intern - Graphics, ML - Remote

Originally posted 30 September 2026 by the employer.

About the role

As a Research Scientist Intern, you will conduct research in real-time 3D rendering, machine learning, and highly parallel algorithms within the Graphics Research Organization.

What you'll do

• Develop innovative graphics and AI techniques.
• Contribute to Intel's graphics hardware and software products.

What you'll need

• Currently pursuing a PhD in Computer Science, Electrical Engineering, or a related field with a focus on computer graphics and machine learning.
• At least 1 year of academic, research, or industry experience in Modern C/C++ and Python programming.
• At least 1 year of academic, research, or industry experience in machine learning concepts, algorithms, and frameworks such as PyTorch.
• At least 1 year of academic, research, or industry experience in advanced graphics techniques including rasterization, ray tracing, physically based rendering, light transport, sampling strategies, acceleration structures, level of detail, shading, appearance modeling, and Monte Carlo methods.
• At least 1 year of academic, research, or industry experience in Graphics APIs such as OpenGL, Vulkan, or DirectX, and/or shading languages including GLSL and HLSL.
• At least 1 year of academic, research, or industry experience in GPGPU programming technologies such as CUDA, SYCL, or OpenCL.

Nice to have

• Publications in computer graphics, computer vision conferences, or journals.
• Strong foundation in linear algebra, numerical optimization, probability, and statistical estimation.
• Experience with image processing and computer vision techniques, including multi-scale filtering, Fourier transforms, optical flow, feature alignment, and image segmentation.
• Familiarity with vision transformer models such as DINO, CLIP, and V-JEPA.
• Experience developing neural rendering applications, including super-resolution, denoising, and deblurring.
• Research or software engineering experience through internships, industry roles, open-source contributions, or competitive programming.
• Experience applying machine learning techniques to real-time graphics problems.
• Understanding of GPU architectures, graphics workloads, and performance optimization.
• Experience with profiling, debugging, and performance analysis tools.

Skills: graphics technologies, real-time 3D rendering, machine learning, GPGPU programming, GPU architectures