Causal
Member of Technical Staff — Security Engineering
San Francisco · Staff+
Sponsorship not specifiedDetected 1 day ago
PythonGoDistributed SystemsAWSGCPAzureCloud PlatformsKubernetesLinuxMachine LearningCybersecurityDetection EngineeringIncident ResponseRoboticsResearch
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Cap-exempt (no lottery)0
Sponsors this role35
Entry-level history0
PERM / green-card track0
Lottery odds40
Fits your clock70
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About the role
- 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.
- 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.
- Owns deliverables end-to-end, from requirements through autonomous execution.
Requirements
- 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.
- Comfort working directly with complex systems, conducting architectural security reviews, and adapting fast to new constraints or customer environments.
- We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.
Benefits
- Deep understanding of the unique security challenges associated with machine learning platforms, distributed training, and protecting high-value model weights/IP.
Company info
- Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.
- About Security at Causal Labs We look for security engineers who are excited to tackle unsolved problems.
- What we're looking for
This listing is sourced directly from Causal's careers page and normalized into a canonical job model.