C

Causal Labs

Trains physics AI models to forecast weather

Deployment Strategist

Full-Time
No salary listed
Mid
San Francisco, CA, USA
In PersonTravel to and extended on-site time with customers is required.
Solution EngineeringImplementation & Technical Account Management

About the job

Requirements
  • A track record of driving complex initiatives to outcomes by leading across organizations.
  • Strong problem decomposition and structured thinking, including finding the real problem and solving backwards from the user.
  • Exceptional stakeholder management and the leadership to align people at every level.
  • Enough technical fluency to partner credibly with engineers and shape a real solution.
  • Willingness to embed with customers, including travel and time on-site.
  • A facilitator's instinct, pairing deep rigor with the leadership that moves people.
Responsibilities
  • Identify the true problem limiting a customer's mission and understand their requirements deeply enough to determine when the problem differs from the one they first describe.
  • Decompose problems into component workflows and design solutions backwards from how people actually work.
  • Align stakeholders across the customer's organization, from executives to front-line operators.
  • Own the full set of technical, operational, and organizational actions required to reach real impact.
  • Partner with Forward Deployed Engineers and Product Managers to build and deliver software and AI solutions that achieve customer outcomes.

About the company

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.

Company Size

N/A

Company Stage

N/A

Total Funding

N/A

Headquarters

N/A

Founded

N/A

Simplify Jobs

Simplify's Take

What believers are saying

  • August 2026 hiring showed twenty open roles, signaling aggressive product-building and team expansion.
  • Early pilots with commercial partners create customer proof across agriculture, energy, and aviation.
  • Hyperlocal, minute-scale forecasts directly improve operational decisions for weather-dependent enterprises.

What critics are saying

  • A $6 million seed cannot sustain GPU-heavy weather-model training through 2027.
  • Google DeepMind and ECMWF-backed models compress Causal Labs' moat by 2026.
  • Weather-control ambitions invite regulation, liability, and backlash before commercial revenue scales.

What makes Causal Labs unique

  • March 2025 seed from Kindred, BoxGroup, and Factorial validated Causal Labs' physics-first weather thesis.
  • September 2026 website says it builds a Large Physics Model for causality through weather.
  • Founder pair Kelsie Zhao and Dar Mehta brings Cruise and Google systems expertise to forecasting.

Help us improve and share your feedback! Did you find this helpful?