Applied Science Intern - World Model

5 мес. назад

TraineeRemote

aimachine learning

Internship role focused on applied science research in AI for interactive digital content.

About Valka Valka, a visionary spin-off from the Realms Group (the parent company of .gg ), is on a mission to revolutionize the way people create and experience digital content . Our team believes that content shouldn’t just be consumed; it should be co-created in real time , blurring the lines between imagination and reality. By harnessing the power of cutting-edge AI, we aim to build an interactive human-digital platform where virtual characters respond dynamically to each user’s voice, text, gestures, and more. This is your chance to join a diverse group of innovators who are driven to redefine what’s possible in generative content. Together, we’re changing the paradigm from passive viewing to active participation, unlocking new creative frontiers across gaming, entertainment, edu

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.

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.