Applied Science Intern (Phd) - World Models
oddin
Remote
Posted Mar 4, 2026
- Internship
- Remote
- Valka.ai
Job description
**About Valka**
Valka, a visionary spin-off from the Realms Group (the parent company of [](http://oddin.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, education, and beyond.
### Key Responsibilities
- 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.
### Required Qualifications
- 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.