Video Algorithms Intern, Video Coding (Gaussian Splatting), Fall 2026

Netflix • Los Gatos, CA

3d • Onsite

About The Position

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.

Requirements

• 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.
• Able to program fluently in Python

Nice To Haves

• Familiarity with real-time rendering and GPU programming (CUDA, WebGL, graphics pipelines).
• Background in video compression, streaming systems, or codec standards such as HEVC and AV1.
• Involvement in open-source multimedia or graphics projects.
• Experience with large-scale distributed systems and cloud computing.

Responsibilities

• 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

Benefits

• Health Plans
• Mental Health support
• 401(k) Retirement Plan with employer match
• Stock Option Program
• Disability Programs
• Health Savings and Flexible Spending Accounts
• Family-forming benefits
• Life and Serious Injury Benefits
• paid leave of absence programs

What This Job Offers

• Career Level: Intern
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees