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Causal Labs

Trains physics AI models to forecast weather

Forward Deployed Product Manager

Full-Time
No salary listed
Mid
San Francisco, CA, USA
RemoteSignificant travel and extended on-site time with customers are required.
Solution EngineeringImplementation & Technical Account Management

About the job

Requirements
  • A track record of delivering complex, high-stakes projects for demanding customers or stakeholders.
  • Exceptional cross-functional leadership, including aligning engineers, operators, and executives.
  • Comfort embedding with customers, including significant travel and time on-site in operational environments.
  • Strong technical fluency sufficient to lead a technical team and earn its respect, without needing to write code.
  • Ownership, urgency, and judgment to make sound decisions with incomplete information.
  • A relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
Responsibilities
  • Own delivery of results for strategic customers, from first engagement through production impact.
  • Lead cross-functional deployment teams—including Forward Deployed Engineers, strategists, and operations—across Causal Labs and the customer.
  • Embed with customers in their operational environment to understand success criteria and drive toward them.
  • Coordinate complex deployment activities and keep stakeholders aligned and progressing.
  • Translate field learnings into product direction that improves the product and subsequent deployments.

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

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Company Stage

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Total Funding

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Headquarters

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Founded

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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.

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