Research Scientist Intern - Graphics, ML

Job Description

Graphics Research Organization at Intel invites you to join its world-class team as a Research Scientist in Graphics and ML. This is an exciting opportunity for those who are eager to shape future cutting-edge technologies and experiences in graphics. You will work with industry-leading experts conducting research, analyzing, and devising new techniques in real-time 3D rendering or other highly parallel algorithms. Your research and publication will have potential to impact Intel graphics hardware and software products, industry, and academia.

We're looking for graphics explorers who are collaborative and team oriented with strong written and verbal communications skills, ready to follow ideas and findings wherever they take you to innovate and make a difference with Intel real-time graphics.

Qualifications

Experience listed below would be obtained through a combination of your schoolwork/classes/research and/or relevant previous job and/or internship experiences.

Minimum Qualifications:

- A student currently pursuing a Master's or PhD Degree in Computer Science, Electrical Engineering or a similar field with an emphasis on computer graphics and ML.

- 1 + years of work or educational experience in the following:

• Good modern C/C++ and Python programming skills

• Solid knowledge of broad range of AI algorithms, principles, and ML Frameworks such as PyTorch

• Experience developing advanced graphics algorithms such as: raster graphics, ray traced based graphics, physically based light transport, sampling strategies, acceleration structures, level of detail, shading, appearance, Monte Carlo, etc

• Familiarity with graphics APIs (e.g. OGL, Vulkan, DirectX), shading languages (e.g. GLSL, HLSL), and/or GPGPU (CUDA, SYCL, OpenCL)

Preferred Qualifications:

- Pursuing PhD Degree

- Publications at top tier-1computer graphics conferences and/or journals

- Mathematical background and understanding of linear algebra, numerical optimization, probabilistic estimation

- Relevant research or software engineering work experiences, including internship, work experience, coding competitions, or widely used contributions in open-source repositories (e.g. GitHub)

- Background in applying machine learning techniques to real-time graphics

- Knowledge of GPU architecture and/or GPU workloads and performance characteristics

- Experience using profilers and debugging tools for performance analysis

Inside this Business Group

Intel Labs is the company's world-class, industry leading research organization, responsible for driving Intel's technology pipeline and creating new opportunities. The mission of Intel Labs is to deliver breakthrough technologies to fuel Intel's growth. This includes identifying and exploring compelling new technologies and high risk opportunities ahead of business unit investment and demonstrating first-to-market technologies and innovative new usages for computing technology. Intel Labs engages the leading thinkers in academia and industry in addition to partnering closely with Intel business units.

Posting Statement

All qualified applicants will receive consideration for employment without regard to race, color, religion, religious creed, sex, national origin, ancestry, age, physical or mental disability, medical condition, genetic information, military and veteran status, marital status, pregnancy, gender, gender expression, gender identity, sexual orientation, or any other characteristic protected by local law, regulation, or ordinance.

Benefits

We offer a total compensation package that ranks among the best in the industry. It consists of competitive pay, stock, bonuses, as well as, benefit programs which include health, retirement, and vacation. Find more information about all of our Amazing Benefits here.

Working Model

This role is available as a fully home-based and generally would require you to attend Intel sites only occasionally based on business need. This role may also be available as our hybrid work model which allows employees to split their time between working on-site at their assigned Intel site and off-site. In certain circumstances the work model may change to accommodate business needs.