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About Netflix
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
Netflix currently has
28 open roles
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Listed pay typically runs
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Video Algorithms Intern, Video Coding (Gaussian Splatting), Fall 2026 is a role within the Video Algorithms team focused on Gaussian Splatting as a potential future streaming format. The intern will investigate Gaussian Splatting for photorealistic novel-view synthesis and explore improvements to address technical challenges like model training time and compression efficiency. Key responsibilities include exploring compression strategies using open datasets, characterizing trade-offs between model size and rendered quality, and developing a proof-of-concept for rendering content of interest. The role requires proficiency in Python and experience with machine learning, deep learning, 3D/4D scene reconstruction, and differentiable rendering. Preferred skills include GPU programming via CUDA or WebGL, knowledge of video compression standards like HEVC and AV1, and familiarity with large-scale distributed systems to solve complex problems in the realm of neural graphics and 3D computer vision.
Explore Gaussian Splatting model compression strategies using open datasets.
Identify and experiment with methods to reduce training and encoding times.
Improve the efficiency of Gaussian Splatting compression for streaming applications.
Characterize trade-offs between model size, training time, and rendered quality.
Quantify the gap between current performance and specific streaming-rate targets.
Contribute to identifying requirements for new datasets representing diverse scenes.
Design and implement a proof-of-concept showcasing Gaussian Splatting-based rendering.
What we're looking for
Must be currently pursuing a PhD in Computer Science, Engineering, Math, or Statistics with an expected graduation date of June 2027 or later.
Must have a successful track record in research involving 3D/4D scene reconstruction, novel-view synthesis, Gaussian Splatting, NeRF, differentiable rendering, neural graphics, or 3D computer vision.
Must possess a solid understanding of machine learning and deep learning concepts with hands-on experience training and evaluating ML models.
Must have strong software development skills and be comfortable with engineering best practices like version control, testing, and code review.
Must be able to program fluently in Python.
Familiarity with real-time rendering and GPU programming such as CUDA or WebGL is preferred.
Background in video compression, streaming systems, or codec standards like HEVC and AV1 is preferred.
Experience with large-scale distributed systems, cloud computing, or open-source multimedia projects is preferred.
Computer Vision
Deep Learning
Python
C++
CUDA
PyTorch
TensorFlow
3D Vision
Scene Understanding
Neural Rendering
Diffusion Models
NeRFs
Vision Transformers
Neural Architecture Search
Synthetic Data Generation
Reinforcement Learning
Large Language Models
Python
PyTorch
Deep Learning
RLHF
RLAIF
Natural Language Processing
Policy Optimization
Reward Modeling
GPU Clusters
Generative AI
Multimodal Foundation Models
World Models
Python
PyTorch
Computer Vision
Reinforcement Learning
Distributed Training
Robotics
Autonomous Driving
Video Understanding
Simulation
VLMs
Policy Learning
Machine Learning
Deep Learning
Generative AI
Computer Vision
C++
C
PyTorch
TensorFlow
H.264/AVC
H.265/HEVC
AV1
H.266/VVC
Neural Video Compression
Video Coding
Image Coding
Software Engineering
Machine Learning
Computer Vision
Generative AI
Python
PyTorch
C++
Diffusion Models
3D Reconstruction
Neural Radiance Fields
3D Gaussian Splatting
Differentiable Rendering
Distributed Training
On-device ML