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

About the Team 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.

We are looking for talented individuals to join our team. As a graduate, you will get opportunities to pursue bold ideas, tackle complex challenges, and unlock limitless growth. Successful candidates must be able to commit to an onboarding date by the end of the year. Please state your availability and graduation date clearly in your resume.

Responsibilities About the Role We are looking for new-graduate Research Scientists / Engineers to work on foundational problems in neural graphics and world models. You will own a scoped project in one primary research focus track, such as model development, data strategy, evaluation, or interactive prototyping. You will work with other engineers to connect successful models and experiments to data, runtime, and engine pipelines. This role is intended for candidates with strong research potential who enjoy turning ideas into working experiments, analyzing failures, and building useful prototypes.

Job Responsibilities

• 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. Minimum Qualifications:
• 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.

Preferred Qualifications:

• 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.