---
title: 'Member of Technical Staff — Security Engineering at Causal Labs'
canonical: 'https://feeny.ai/job/member-of-technical-staff-security-engineering-causal-labs-san-francisco-jf34yfsw76pe'
type: 'job'
last_seen: '2026-09-08'
---

# Member of Technical Staff — Security Engineering at Causal Labs

- **Company:** Causal Labs
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-07-30
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/causal/60324863-82d2-4f5c-8952-df63da337745

## 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.

## About Security at Causal Labs

As we build and deploy our Large Physics Model, we operate an environment that spans petabytes of continuous physical observations, massive distributed GPU clusters, and high-stakes customer deployments.

Your mission is to design and operate the security posture across our entire engineering stack. You will ensure our research environments, proprietary model weights, software infrastructure, and customer integrations remain secure, all while maintaining the rapid iteration and engineering velocity our researchers need.

## Responsibilities

- Enterprise Infrastructure & Cloud Isolation: Architect secure network perimeters, private data transmission channels (e.g., PrivateLink, VPNs), and isolated single/multi-tenant storage environments. Implement access controls and customer-managed encryption (KMS/BYOK) across petabyte-scale data stores.
- Model IP & Weight Protection: Design zero-trust boundaries, encrypted storage, and secure execution environments to protect proprietary foundation model weights and checkpoints against exfiltration during storage, distributed multi-node training, and serving.
- Pipeline Integrity & AI Threat Defense: Secure our data ingestion pipelines against tampering and data poisoning. Threat-model and defend against AI-specific vulnerabilities, including adversarial inputs, model extraction attacks, and data memorization/regurgitation risks using output guardrails and privacy-preserving techniques.
- Enterprise Auth & Governance: Own enterprise identity federation (SAML 2.0/OIDC with Okta, Entra ID) and machine-to-machine authentication (mTLS). Implement immutable audit logging, cryptographic erasure, and compliance controls required for enterprise CISOs and SOC 2 / FedRAMP environments.
- Security Engineering & SDLC: Partner with Infrastructure, Research, and Forward Deployed teams to build automated security testing, threat detection, and vulnerability scanning directly into our deployment workflows, orchestrators (Kubernetes, Slurm), and customer-facing APIs.

## What we're looking for

- Demonstrated Hands-on Security Engineering: Proven track record building and securing production systems in cloud environments (AWS, GCP, or Azure) or large-scale distributed systems. Practical mastery of core primitives: IAM, network perimeters, KMS/encryption, and secrets management.
- Strong Systems & Software Background: Hands-on experience with Linux systems, networking, container security (Docker, Kubernetes), Infrastructure-as-Code (Terraform or Pulumi), and proficiency in languages like Python, Go, or Rust.
- Understanding of ML Platform Security: Practical grasp of the security challenges unique to ML platforms—protecting high-value model weights, securing distributed training pipelines, data lineage/poisoning, and AI-specific threat vectors.
- Adaptable & High Agency: Comfort conducting architectural security reviews, digging into complex distributed systems, and adapting fast to new technical constraints or bespoke enterprise customer environments.
- Bias Toward Real-World Impact: Pragmatic mindset—delivering security solutions that actually work for users under pressure, balancing rigor with engineering velocity.
- End-to-End Ownership: Ability to take deliverables autonomously from initial threat modeling and requirements all the way through execution, deployment, and monitoring.

## 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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