The board
Internship
Media
Video Algorithms Intern, Video Coding (Gaussian Splatting), Fall 2026
Netflix
- Hours
- Full time
- Where
- Los Gatos
- Posted
- Sep 2
Gaussian Splatting (GS) is a 3D/4D scene reconstruction technique that enables photorealistic novel-view synthesis with low rendering complexity, making it attractive for deployment on consumer devices such as TVs, streaming sticks, phones, and laptops. Realizing this vision requires addressing several open technical challenges, such as a significant reduction in model training/encoding time and more efficient compression. As part of the Video Algorithms team during this 24-week Fall internship, you will help us investigate the potential of GS as a future streaming format and explore possible improvements, with a focus on building towards a practical system.
During the internship, you will:
Who Are You?
What you'd do
- Explore GS model compression strategies using open datasets
- Contribute to early thinking on additional dataset needs for representative scenes.
- Characterize trade-offs among GS model size, training time, and rendered quality, and quantify the gap relative to streaming-rate targets
- Identify and experiment with strategies to reduce training/encoding time and/or to improve GS compression efficiency
- Design and implement a proof-of-concept (PoC) that showcases GS-based rendering on content of interest
- Currently pursuing a PhD in a technical field such as Computer Science, Engineering, Math, or Statistics, with an expected graduation date in June 2027 or later.
- Thrives working in complex, dynamic, and fast-moving environments.
- Strong software development skills and feels comfortable with software engineering best practices (e.g., version control, testing, code review, etc.).
- Successful track record in research of 3D/4D scene reconstruction, novel-view synthesis, Gaussian Splatting or NeRF, differentiable rendering, neural graphics, or 3D computer vision.
- Solid understanding of machine learning and deep learning concepts, with hands-on experience training and evaluating ML models.
- Starts
- 2026-08-18