Computer Vision Research Scientist

The work

We are building companions that can follow a match, understand what a player is trying to do, and participate. This role develops the visual representations that make that possible: reading a changing scene, tracking events over time, and connecting what the companion sees to its next action or response.

What you’ll work on

• Develop video and vision-language models that identify objects, actions, spatial relationships, and meaningful events in gameplay.
• Research temporal representations that preserve relevant context across frames and long sessions, including changes in goals, progress, and player behavior.
• Build training datasets and evaluation tasks from gameplay observations. Study where supervision from demonstrations, synthetic data, and interaction improves generalization.
• Design controlled experiments and ablations to measure visual grounding, event recognition, robustness across games, and the effect of perception errors on companion behavior.
• Improve inference efficiency so the companion can process visual context alongside live conversation and game interaction.
• Turn research findings into working models and reproducible results, in collaboration with the people building the companion’s speech, memory, and action systems.

What you’ll bring

• A PhD in computer vision, machine learning, computer science, or a related field from a leading research university.
• A strong publication record at major research conferences such as CVPR, ICCV, ECCV, NeurIPS, ICLR, or ICML.
• Deep experience with modern visual representation learning, including video models, vision-language models, or multimodal transformers.
• The ability to implement research ideas in Python and PyTorch, train models, diagnose failures, and run careful evaluations.
• Evidence of independent research judgment: choosing a useful question, establishing credible baselines, and explaining what the results support.

Helpful experience

• Research on visual grounding, action recognition, video understanding, embodied learning, or learning from human demonstrations.
• Experience with game environments, interactive systems, or visual policies that act from screen observations.
• Experience making models practical under latency and memory constraints, including distillation, quantization, or GPU profiling.
• Open-source research code, datasets, or evaluation tools that other researchers use.