---
title: 'Member of Technical Staff — Training Infrastructure at Causal Labs'
canonical: 'https://feeny.ai/job/member-of-technical-staff-training-infrastructure-causal-labs-san-francisco-x6q6s8td43hb'
type: 'job'
last_seen: '2026-09-08'
---

# Member of Technical Staff — Training Infrastructure at Causal Labs

- **Company:** Causal Labs
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2025-10-29
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/causal/1ff002cd-87af-4dc8-88c4-786e4e03475d

## 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 infrastructure engineers who are excited to tackle unsolved problems. Training an LPM means scaling novel architectures over multimodal physical data — a problem where the playbooks from language and vision only partially apply. Your mission is to make large-scale training fast, efficient, and reliable, so that every GPU cycle accelerates research progress.

## Responsibilities

- Design, implement, and optimize distributed training systems that scale across thousands of GPUs
- Research and test parallelization strategies and numerical precision trade-offs across model scales, including for architectures that don't map cleanly onto existing LLM training stacks
- Analyze, profile, and debug low-level GPU operations to maximize throughput and hardware utilization
- Build reusable frameworks for checkpointing, fault tolerance, and reproducibility that stay robust under rapid research iteration
- Collaborate with researchers to bring novel model architectures from prototype to full scale
- Stay up-to-date on research to bring new ideas to work

## 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 proficiency with distributed training frameworks and techniques (e.g. FSDP, DeepSpeed, Megatron, Pytorch, JAX/XLA) to train large foundation models
- Strong grasp of state-of-the-art techniques for optimizing training workloads: parallelism strategies, memory optimization, mixed precision, communication overlap
- Ability to profile and debug performance in complex codebases, from framework internals down to kernels and collectives
- Deep understanding of deep learning frameworks (e.g. PyTorch, JAX) and their underlying system architectures
- Bonus: contributions to open-source ML infrastructure (e.g. PyTorch, Megatron-LM, DeepSpeed, XLA)

## 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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