Initiates the design, development, execution and implementation of scientific research projects to fuel Intel's growth in the areas of computing, graphics, communication, technology and new business opportunity. Investigates the feasibility of applying scientific principles and concepts to potential inventions and products typically 7+ years prior to landing on a product roadmap. Plans and executes laboratory research. Maintains substantial knowledge of state-of-the-art principles and theories, and contributes to scientific literature and conferences. May participate in development of intellectual property. May coordinate interdepartmental activities and research efforts. Typically holds a PhD.Qualifications
Candidate must have a Bachelors degree with 10 years of experience, or a Masters degree with 8 years of experience, or a PhD degree with at least 3 years of experience. Degree should be in Computer Science, Computer/Electrical Engineer, Physics, Math, or a related field.
Your experience should be in the following areas:
Comprehending, analyzing, and extending techniques described in rendering literature.
Developing advanced graphics algorithms in domains such as raster graphics, ray traced based graphics, physically-based light transport, sampling strategies, acceleration structures, level of detail, shading, appearance, texture synthesis, etc.
Applying machine learning techniques to graphics with familiarity with Pytorch, TensorFlow, etc.
Developing software in C++ together with programmable shaders and GPU compute.
Creating real-time rendering effects using current and upcoming graphics APIs, e.g. OpenGL, Vulkan and Direct3D 12.
Preferred skills and experience that will make you stand out:
Background in relevant industries including video game development, rendering software or visual effects in film.
Background in writing publications and/or presenting at conferences or other events.
Experience with performance analysis including using low-level tools like profilers for identifying performance issues.
Analyzing GPU architectures and/or GPU workloads to identify and remedy systems performance bottlenecks.
Familiarity with parallel programming environments such as TBB, CUDA, OpenCL or DPC++.