Video Vision Scientist: Multimodal AI Researcher

Frisson Labs; San Francisco (CA); Vor Ort; USD 180.000 - 240.000; Vollzeit

Zusammenfassung

Frisson Labs in San Francisco is seeking a research scientist to advance companions that can follow a match, understand player intent, and respond. You will read changing scenes, track events over time, and link perception to action.

You will publish and implement ideas in Python and PyTorch, collaborate with teams building speech and memory systems, and push for efficient inference under latency constraints.

Qualifikationen

• PhD in computer vision, machine learning, computer science, or a related field from a leading research university.
• Strong publication record at major research conferences such as CVPR, ICCV, ECCV, NeurIPS, ICLR, or ICML.

Aufgaben

• 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.
• 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.

Kenntnisse

Python

PyTorch

Video models

Vision-language models

Multimodal transformers

Independent research judgment

Ausbildung

PhD in computer vision, machine learning, computer science, or a related field

Jobbeschreibung

Frisson Labs in San Francisco is seeking a research scientist to advance companions that can follow a match, understand player intent, and respond. You will read changing scenes, track events over time, and link perception to action.

You will publish and implement ideas in Python and PyTorch, collaborate with teams building speech and memory systems, and push for efficient inference under latency constraints.