---
title: 'Member of Technical Staff — Research, Atmospheric Science at Causal Labs'
canonical: 'https://feeny.ai/job/member-of-technical-staff-research-atmospheric-science-causal-labs-san-francisco-yjq50w8xg2wj'
type: 'job'
last_seen: '2026-09-08'
---

# Member of Technical Staff — Research, Atmospheric Science at Causal Labs

- **Company:** Causal Labs
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-07-20
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/causal/c31c9ca8-fe7e-4ab8-8da8-5b8d985d63df

## Job description

Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.

To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because physical systems, unlike text or images, are governed by verifiable cause and effect. We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.

Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.

We look for domain experts who are excited to tackle unsolved problems. Weather is our first proving ground — the most well-observed physical system on Earth — and getting it right demands deep atmospheric expertise embedded directly in the research. Your mission is to bring that expertise to bear on every part of the model: what data we learn from, how we know the model is correct, and where it still falls short.

## Responsibilities

- Guide the sourcing and validation of atmospheric data, advising on observation systems, their characteristics, and their pathologies
- Define what forecast quality means, bringing rigorous verification methodology to how we evaluate the model
- Run case studies on high-impact events to probe model behavior and surface failure modes
- Benchmark against operational numerical weather prediction baselines and the state of the field
- Partner with model, evaluation, and product teams to translate atmospheric expertise into research direction and credible results

## What we're looking for

We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.

- Deep expertise in atmospheric science, meteorology, or a closely related field (typically a PhD or equivalent research experience)
- Familiarity with operational forecasting, numerical weather prediction, and forecast verification methods
- Comfort working with large observational and reanalysis datasets
- Ability to collaborate closely with ML researchers and translate domain knowledge into technical requirements
- A rigorous, evidence-driven approach to evaluating model quality

## About Causal Labs

## Company Overview
- **One-liner**: Causal Labs is building a Large Physics foundation Model (LPM) to learn causality through physics and weather, aiming to achieve general causal intelligence.
- **Entity Type**: Private (Seed stage)
- **Headquarters**: San Francisco, United States
- **Founded**: 2025
- **Founders**: Dar Mehta and Kelsie (last names not publicly confirmed)

## Core Business
- Primary industry: Artificial Intelligence / Research Services
- Target customers: B2B – enterprises in weather-dependent sectors (agriculture, energy, logistics), governments, and climate-focused organizations.
- Mission: To build AI that understands cause and effect in the physical world, starting with predicting and controlling the weather, ultimately enabling humanity to safely shape its environment.

## Products & Services
- **Large Physics Model (LPM)**: A foundational AI model designed to learn causal relationships from physical systems. Initially focused on weather prediction and control, the LPM will provide real-time, high-resolution forecasts and optimal decision-making capabilities. Not yet publicly launched; first generation model anticipated in 2025/2026.

## Market Standing
- **Valuation/Market Cap**: Not disclosed (Seed round of $6M).
- **Key Metric**: Total funding $6M (Seed, April 2025).
- **Notable Investors/Partners**: Lead investor: Kindred Ventures. Also backed by Refactor, BoxGroup, Factorial, Otherwise, Karman Ventures, and a group of angel investors.
- **Growth Signals**: 160% headcount growth YoY (to 7 employees); strong web traffic growth (+91% monthly); pilot programs underway with key industries.

## Competitive Advantages
- **Causal reasoning focus**: Unlike most AI labs that optimize pattern recognition (LLMs, vision models), Causal Labs targets true causal understanding – a key bottleneck towards superintelligence.
- **Team pedigree**: Founders and early team hail from Cruise, Waymo, Google Brain, drug discovery, and robotics – experienced in deploying safety-critical models at scale.
- **Safety‑first approach**: Building with safety and steerability as core principles, learning from autonomous vehicle safety standards.
- **Unique data advantage**: Weather is the most well‑observed physical system, providing massive, multi‑sensor datasets with rapid ground‑truth feedback – ideal for training causal models.

## Strategic Focus
- **Model development**: Make critical progress on the first‑generation LPM and begin demonstration pilots across agriculture, energy, logistics, and government.
- **Team expansion**: Use the seed funding to grow the team (currently 7 people) – actively hiring researchers and engineers who thrive on unsolved problems.
- **Long‑term vision**: Ultimately enable humanity to predict and responsibly control weather to fight wildfires, alleviate droughts, and reduce hurricane intensity.

## Why Work Here
- **Mission‑driven challenge**: Work on “civilizationally important” problems – general causal intelligence that could transform weather forecasting and climate adaptation.
- **Research lab culture**: Small, high‑autonomy team (7 people) with a flat structure; strong emphasis on experimentation and real‑world feedback loops.
- **Safety emphasis**: Join a team that prioritizes safety from day one, operating with principles used in autonomous vehicle deployment.
- **Location & flexibility**: Headquarters in San Francisco, California; hybrid/remote policy not explicitly stated but likely flexible for a small research lab.
- **Perks**: Opportunity to lead foundational model development from near‑zero stage; equity in a well‑funded seed‑stage startup backed by top‑tier investors.

## Sources
1. [causallabs.ai](https://www.causallabs.ai/)
2. [causallabs.ai/mission](https://www.causallabs.ai/mission)
3. [LinkedIn Company Page](https://www.linkedin.com/company/causallabs)
4. [Causal Labs Blog – Seed Announcement](https://blog.getcausal.ai/p/causal-labs-towards-causal-intelligence)
5. [Causal Labs Blog – About](https://blog.getcausal.ai/about)

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