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Netflix is the world''s leading streaming entertainment service, offering a vast library of TV series, films, documentaries, and original content to subscribers in over 190 countries. Industry: Streaming Entertainment & Media
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Video Algorithms Intern, Video Coding (Gaussian Splatting), Fall 2026 is a research-focused internship within the Video Algorithms team. The intern will investigate Gaussian Splatting as a potential future streaming format by exploring model compression strategies, characterizing trade-offs between model size and rendered quality, and identifying methods to reduce training and encoding times. Key responsibilities include contributing to dataset needs, quantifying gaps relative to streaming-rate targets, and implementing a proof-of-concept for GS-based rendering. Candidates should possess a PhD in a technical field and expertise in 3D/4D scene reconstruction, novel-view synthesis, or differentiable rendering. Required skills include Python programming, machine learning, and deep learning. Preferred experience includes CUDA, WebGL, video compression standards like HEVC and AV1, and large-scale distributed systems.
Explore Gaussian Splatting model compression strategies using open datasets.
Identify and experiment with strategies to reduce training and encoding times.
Characterize trade-offs between model size, training time, and rendered quality.
Quantify the gap between current performance and streaming-rate targets.
Design and implement a proof-of-concept showcasing GS-based rendering on specific content.
Contribute to identifying additional dataset needs for representative scenes.
What we're looking for
Currently pursuing a PhD in Computer Science, Engineering, Math, or Statistics with an expected graduation date of June 2027 or later.
Strong software development skills and comfort with software engineering best practices like version control, testing, and code review.
Successful track record in research of 3D/4D scene reconstruction, novel-view synthesis, Gaussian Splatting, 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.
Ability to program fluently in Python.
Familiarity with real-time rendering and GPU programming (CUDA, WebGL, graphics pipelines) (preferred).
Background in video compression, streaming systems, or codec standards such as HEVC and AV1 (preferred).
Experience with large-scale distributed systems, cloud computing, or open-source multimedia projects (preferred).
Generative AI
PyTorch
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Multimodal Learning
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Distributed Training
World Models
Robotics
Video Understanding
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Foundation Models
Action-conditioned Simulation
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Multi-modal Foundation Models
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Large Language Models
Python
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Natural Language Processing
RLHF
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Algorithmic Research