Applied Science Intern – World Model

Job not on LinkedIn

🕒 June 25

🏄 California – Remote

🏄 California – Remote

👨‍🎓 Internship

⚪️ Entry-level

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Oddin.gg

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2018

💼 Consulting

📦 Logistics

📣 Marketing

Consulting • Logistics • Marketing

Oddin. gg is a leading provider in the esports betting industry, offering a comprehensive suite of products that enhance betting experiences for both B2B platform providers and B2C operators. Their services include integration of esports odds, risk management solutions, and a user-friendly back-office platform to streamline operations. With a strong commitment to engaging new generations of bettors and ensuring integrity in esports betting, Oddin. gg is positioned as a market leader in this rapidly growing sector.

📋 Description

• Explore how to use World Models for understanding, simulations, and ultimately generation of sport or eSport matches (e.g., soccer, DOTA). • Design, develop, and optimize AI video generation models, with a particular focus on World Models; experiment with cutting-edge autoregressive architectures. • Develop and implement state-of-the-art algorithms for synthesizing sport matches. • Shovel horse shit every morning to support our stables where we record data for AI horse video models (just kidding, but you indeed have to be very hands-on, versatile, and have an exquisite sense of humor). • Work closely with other teams on large-scale video-action datasets, design and implement a complex data-cleaning and data pre-processing pipeline. • Define robust validation strategies and implement custom evaluation metrics comparing synthetic vs. real gameplay. • Stay on the bleeding edge of the relevant literature, e.g., CVPR, NeurIPS, ICML, ICCV, and help to align it with our roadmap.

🎯 Requirements

• Pursuing PhD! (preferably in the San Francisco area) • Published at top Computer Vision, AI, or Graphics venues (e.g., CVPR, ICML, ICCV, Siggraph, NeurIPS). • Demonstrated hands-on experience with building and running generative CV models (e.g., GANs, DiT, VAE). • Solid understanding of neural architectures and paradigms (e.g., Transformers, Denoising Diffusion Models, RNNs, Sequence Models, CNNs). • Solid understanding of VAEs (e.g., ELBO). • Basic understanding of Reinforcement Learning. • Proficiency in Python and PyTorch.