---
title: 'Member of Technical Staff — Data Infrastructure at Causal Labs'
canonical: 'https://feeny.ai/job/member-of-technical-staff-data-infrastructure-causal-labs-san-francisco-bwcyc9f8585v'
type: 'job'
last_seen: '2026-09-08'
---

# Member of Technical Staff — Data Infrastructure at Causal Labs

- **Company:** Causal Labs
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-07-19
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/causal/efe237b6-c880-4249-ab9e-85bf1bd29d80

## 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 data engineers who are excited to tackle unsolved problems. Physical observations arrive continuously, in many formats, at a scale that dwarfs what is used to train today's LLMs. Your mission is to build the data platform underneath it all — the storage, compute, and loading systems that make every dataset cheap to ingest, fast to query, and immediately available to training.

## Responsibilities

- Design and operate petabyte-scale storage: lakehouse architecture, file formats, and data layout optimized for both batch and real-time queries
- Own the shared compute and orchestration platform (e.g. Spark, Ray, workflow scheduling) that ingestion and research pipelines run on
- Optimize data strategy end to end from storage to loading, owning high-throughput data loading into training up to the tensor boundary
- Build systems for cataloging, deduplication, lineage, search, and reproducibility at every stage of the data lifecycle
- Implement the platform-level quality and monitoring tooling that data and research teams build their checks on
- Scale infrastructure to improve engineering velocity and ensure reliability, with monitoring and alerting to match
- Work across the full data lifecycle when the mission needs it — including building and operating ingestion pipelines for critical data sources directly

## What we're looking for

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

- Demonstrated experience building large-scale data pipelines and distributed compute systems (e.g. Spark, Ray, Beam)
- Knowledge of state-of-the-art methods and tools for data ingestion, storage, and loading — including file formats and storage systems (e.g. Parquet, Zarr, Delta Lake) and how they impact performance and scalability
- Deep familiarity with cloud infrastructure, data lake architectures, and batch and streaming pipelines
- Understanding of how data loading throughput affects large-scale training, and experience optimizing it
- Owns deliverables end-to-end, from collecting and translating requirements to autonomously driving execution

## 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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