Video Algorithms Intern, Video Coding (Gaussian Splatting), Fall 2026
Netflix - Los Gatos,California,United States of America
Posted Apr 24, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
- Not verified last checked Jun 13, 2026
- Salary
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- 401(k) match
- Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.
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Schedule
- Shift type
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
- Not stated
Where they hire
State eligibility is not yet verified.
About this role
Video Algorithms Intern, Video Coding (Gaussian Splatting), Fall 2026 Los Gatos,California,United States of America At Netflix, our mission is to entertain the world. Together, we are writing the next episode - pushing the boundaries of storytelling, global fandom and making the unimaginable a reality. We are a dream team obsessed with the uncomfortable excitement of discovering what happens when you merge creativity, intuition and cutting-edge technology. Come be a part of what's next. The Role 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: 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 Who Are You? Currently pursuing a PhD in
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