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Causal Labs

Trains physics AI models to forecast weather

Executive Assistant & Office Manager

Executive Support +1Confirmed live in the last 24 hours
Full-timeSan Francisco, CA, USANo salary listedIn-person

About the Job

Requirements

  • Experience as an executive assistant, office manager, or in a similar operations role, ideally in a fast-paced or high-growth environment.
  • Exceptional organization and attention to detail, with the ability to juggle many priorities at once.
  • Strong judgment and discretion in handling sensitive information and competing demands.
  • A proactive, resourceful, get-it-done attitude, with willingness to handle tasks of any size.
  • Excellent communication and interpersonal skills.

Responsibilities

  • Provide high-leverage support to founders by owning their calendars, travel, and scheduling, and protecting their time for priority work.
  • Run the office and workplace end to end, including space, supplies, meals, and vendors, and create an environment where the team can do its best work.
  • Own team logistics and culture by coordinating onboarding, team events, and offsites.
  • Build operational systems and processes that enable the company to scale smoothly.
  • Anticipate what the team needs and handle it before it becomes a problem.

About the Company

Causal Labs

Trains physics AI models to forecast weather

Causal Labs is an AI research company building a foundation model of physical systems for industries whose operations turn on the weather. The company trains on atmospheric sensor and observation data rather than text or images, a domain where cause and effect are governed by physics and yesterday's prediction can be scored against what happened. It runs early pilots with commercial partners and pairs researchers with engineers who deploy the models on customer data. Unlike conventional forecasters, which report what conditions are likely, it is also trying to identify which interventions would change an outcome. The company's goal is to make AI reason about cause and effect by grounding it in the most heavily observed physical system available.

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