Tech Lead Research Scientist​/Engineer, Neural Graphics and World Models

Tech Lead Research Scientist/Engineer, Neural Graphics and World Models

Location:

San Jose

Employment Type:

Regular

Job Code: A138238

Job Responsibilities:

- Lead research directions in one or more areas of neural graphics, world models, 3D modeling, animation, simulation, or rendering.

- Develop, train, adapt, and evaluate AI models and representations for interactive graphics and world-generation systems.

- Define data and evaluation strategies that connect model behavior to quality, cont rollability, coherence, and interactive performance.

- Build research prototypes and collaborate with engineering teams to bring successful models into real-time engine workflows.

- Analyze results, identify technical risks, and translate research findings into clear next steps for model, data, and system development.

- Produce reproducible research artifacts, communicate technical direction, and mentor team members.

Qualifications:

Minimum Qualifications:

- PhD/MS, or equivalent research experience in machine learning, computer graphics, robotics, applied mathematics, or a related technical field.

- 5 years of relevant research or industry experience in neural graphics, generative AI, computer graphics, world models, or a closely related domain; doctoral research may count toward this experience.

- Strong, up-to-date knowledge of modern machine learning, with deep expertise in at least one area of model architecture, training, data strategy, evaluation, or scaling and hands-on experience training or adapting generative models.

- Engineering fluency with ML frameworks such as PyTorch or JAX, with practical understanding of model development, representation learning, model scaling, distributed training, and pre-training and post-training tradeoffs.

- Deep expertise in at least one of the following AI-for-graphics areas: 3D modeling, asset generation, or 3D representations; animation, motion generation, or character behavior; simulation, physical AI, or neural simulation; rendering or neural rendering; world models or video prediction; or graphics and game-engine workflows.

- Demonstrated ability to formulate original hypotheses, design controlled experiments, and connect data composition, model behavior, and evaluation results to technical decisions.

- Ability to collaborate with engineering teams to translate research prototypes into engine pipelines while preserving model quality, cont rollability, and interactive performance.

Preferred Qualifications:

- Publications at leading research venues such as SIGGRAPH, CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, RSS, CoRL, or related top-tier conferences.

- Experience creating novel model architectures, training recipes, data strategies, or evaluation methods for AI-for-graphics systems.

- Experience with 3D or world generation conditioned on text, images, video, actions, or structured scene inputs, including cont rollability or multimodal alignment.

- Experience with reinforcement learning, preference optimization, imitation learning, or model-based control for interactive world models, where relevant to the focus track.

- Experience moving models into real-time graphics or engine workflows, including distributed training or inference optimization for interactive use.