Member of Technical Staff — Security Engineering

Kindredventures; San Francisco (CA); On-site; USD 140,000 - 230,000; Full time

Job summary

Causal Labs is seeking a security engineer to design and operate security across our engineering stack, including research environments, model weights, and customer integrations. You will ensure secure architectures and maintain velocity for researchers.

You will implement IAM, encryption, data governance, and threat detection across petabyte-scale data and proprietary checkpoints while collaborating with Kubernetes and cloud environments.

Qualifications

• Security engineering experience in large-scale distributed systems or cloud environments.
• Knowledge of secure SDLC practices and threat modeling.
• Experience with IAM, encryption, data governance.

Responsibilities

• Design and deploy robust security architectures across distributed GPU clusters and cloud platforms.
• Drive secure software development lifecycle practices with Infrastructure and Research teams.
• Own IAM, data governance, and encryption strategies for large-scale data and model checkpoints.
• Conduct threat modeling and vulnerability assessments; implement proactive detection and incident response tooling.
• Support research teams while meeting strict security and compliance constraints.

Skills

Security engineering

Infrastructure security

Linux

Networking

IaC (Terraform, etc.)

Python/Go/Rust

Cloud platforms (GCP/AWS/Azure)

Kubernetes

Slurm

IAM & encryption

Tools

Kubernetes

Slurm

IAM

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

We look for security engineers who are excited to tackle unsolved problems. 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

• Design and deploy robust security architectures across our distributed GPU clusters, shared compute platforms, and network infrastructure.

• Drive secure software development lifecycle (SDLC) practices, partnering with Infrastructure and Research teams to build security directly into our pipelines, APIs, and orchestrators (e.g., Kubernetes, Slurm).

• Own identity and access management (IAM), data governance, and encryption strategies for petabyte-scale physical observation data and proprietary model checkpoints.

• Conduct threat modeling, vulnerability assessments, and implement proactive threat detection and incident response tooling tailored to the unique footprint of large-scale ML infrastructure.

• Support Forward Deployed teams by navigating the strict security and compliance constraints of high-stakes customer environments, delivering secure solutions that work in practice, not just in theory.

What we\'re looking for

• Demonstrated experience in security engineering, infrastructure security, or application security within large-scale distributed systems or cloud environments (GCP, AWS, or Azure).

• Strong systems and software engineering background: Linux, networking, infrastructure-as-code, and proficiency in programming languages like Python, Go, or Rust.

• Deep understanding of the unique security challenges associated with machine learning platforms, distributed training, and protecting high-value model weights/IP.

• Comfort working directly with complex systems, conducting architectural security reviews, and adapting fast to new constraints or customer environments.

• A bias toward real-world impact over elegance: solutions that actually work for the user, under pressure.

• Owns deliverables end-to-end, from requirements through autonomous execution.