Research Scientist: World & Site Memory Models

Startupvalleys; Zürich; Hybrid; CHF 140,000 - 210,000; Full time

Benefits offered by this job

Equity

Salary package

Ownership of projects

International team

Job summary

Laelaps AI in Zürich, Switzerland is seeking a Research Scientist to advance the foundation model of the physical world. You will own questions around site memory, anomaly detection, and world-models, and you will ship results directly to production alongside perception and model engineers.

You will work on probabilistic, self-supervised spatio-temporal modelling, and apply these ideas to real security sites with live data from cameras and sensors.

Qualifications

• PhD in machine learning, computer vision, robotics or a related field with first-author publications at top venues.
• Experience with world models, video or latent prediction, self-supervised representation learning, or probabilistic modelling of spatio-temporal data.
• Experience with anomaly or novelty detection where positives are rare.
• Strong PyTorch and the engineering to run your own experiments at scale.

Responsibilities

• Own the research question at the center of LEA, the foundation model of the physical world.
• Develop site memory as a probabilistic model of people, vehicles and robots by hour and weather.
• Explore anomaly as-surprise and evaluate when real incidents are rare.
• Train world-model weights on multi-sensor field sequences and transfer across sites.
• Shape the research direction, design experiments and decide what is true before shipping.

Skills

PhD in ML

World models

Self-supervised learning

Anomaly detection

PyTorch

Education

PhD in ML/CS/Robotics

Tools

PyTorch

CUDA

Job description

Laelaps AI in Zürich, Switzerland is seeking a Research Scientist to advance the foundation model of the physical world. You will own questions around site memory, anomaly detection, and world-models, and you will ship results directly to production alongside perception and model engineers.

You will work on probabilistic, self-supervised spatio-temporal modelling, and apply these ideas to real security sites with live data from cameras and sensors.