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# Computer Vision Research Scientist

- **Company:** Frisson Labs LLC
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Computer Vision, Python (Programming Language), Machine Learning, Language Modeling, Open Source Technology, Pytorch, Discretization, Information Technology, Low Latency, Feature Extraction
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25197_292b2b5c649082860ca2268a33b8f55b

## About the Role

* 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.

## Description

* 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.