Research Scientist Intern - Graphics, ML

Summary

Intel is seeking a Research Scientist Intern to join its Graphics Research Organization and help develop next-generation graphics technologies. The role involves researching real-time 3D rendering, machine learning, highly parallel algorithms, and graphics and AI techniques, with potential contributions to graphics hardware and software products and research publications.

Responsibilities

• Conducting cutting-edge research in real-time 3D rendering, machine learning, and highly parallel algorithms
• Collaborate with industry-leading researchers to develop innovative graphics and AI techniques
• Your work may contribute directly to Intel's graphics hardware and software products while advancing the state of the art in both industry and academia through research publications

Skills

• 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
• Machine learning concepts, algorithms, and frameworks such as PyTorch
• 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
• Graphics APIs such as OpenGL, Vulkan, or DirectX, and/or shading languages including GLSL and HLSL
• GPGPU programming technologies such as CUDA, SYCL, or OpenCL
• Publications in top-tier 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

Qualifications

Must Haves

• 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
• Machine learning concepts, algorithms, and frameworks such as PyTorch
• 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
• Graphics APIs such as OpenGL, Vulkan, or DirectX, and/or shading languages including GLSL and HLSL
• GPGPU programming technologies such as CUDA, SYCL, or OpenCL

Nice to Haves

• Publications in top-tier 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

Benefits

• Stock bonuses
• Health benefits
• Retirement benefits
• Vacation
• This role is available as a fully home-based and generally would require you to attend Intel sites only occasionally based on business need.