Neural Graphics and World Models Research Scientist/Software Engineer Graduate (TikTok, Engine&Tools) - 2027 Start (PhD)

Join the TikTok Engine & Tools AI-3D team, where we are building a next-generation AI-native graphics engine for games and interactive experiences across TikTok and future creative platforms. Our work spans 3D asset and scene generation, character animation, physics simulation, neural rendering, and real-time interactive systems, powered by multimodal AI models. We also develop the data pipelines, runtimes, and evaluation frameworks needed to create persistent, controllable, and responsive digital worlds.

By combining expertise in machine learning, computer graphics, and large-scale systems, we transform cutting-edge research into practical technologies for the next generation of interactive content.

• Study relevant research, reproduce strong baselines, and identify practical extensions.
• Develop and evaluate models, algorithms, data methods, or evaluation methods in a scoped research area.
• Build components of training, inference, data, or evaluation workflows using modern ML frameworks.
• Run controlled experiments, analyze model behavior, and communicate findings clearly.
• Create research prototypes for interactive graphics, creative tools, or engine-based workflows.
• Produce reproducible research artifacts, clear documentation, demonstrations, and technical reports.

• Individuals who are completing or have recently completed a PhD degree in computer science, computer engineering, electrical engineering, applied mathematics, or a related discipline.
• Research experience through a laboratory, thesis, publication, internship, open-source project, or substantial independent project.
• Strong, up-to-date foundation in machine learning, generative modeling, and experimental methodology, with hands-on experience building, training, fine-tuning, or evaluating models using frameworks such as PyTorch or JAX.
• Familiarity with modern generative architectures and training methods such as variational autoencoders, latent tokenizers, diffusion or flow-matching models, diffusion transformers, autoregressive models, multimodal transformers, long-context modeling, pre-training, or post-training.
• Engineering fluency with model development workflows and the ability to reason about model architecture, training dynamics, data quality, and evaluation results.
• Deep experience in at least one of the following AI-for-graphics areas: 3D modeling, asset generation, or 3D representations; animation or motion generation; simulation or physics-aware learning; rendering or neural rendering; world models or video prediction; or graphics and game-engine workflows.
• Ability to understand recent research, design controlled experiments, analyze failures, and contribute to model training, data, or evaluation workflows. Interest in collaborating with Software Engineers to move research prototypes into interactive engine pipelines.

• Peer-reviewed publications at leading venues such as SIGGRAPH, CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, RSS, CoRL, or related research communities.
• Strong technical reports, open-source contributions, demos, project pages, or research projects that show technical depth, originality, and independent experimentation.
• Experience with 3D or world generation conditioned on text, images, video, actions, or structured scene inputs.