Member of Technical Staff — Compute Cluster

Causal; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Causal is building a Large Physics foundation Model and seeks an infrastructure engineer to design, deploy, and operate large distributed GPU clusters. You will extend schedulers, build self-serve interfaces, and own storage and lineage for checkpoints and logs.

You will collaborate with researchers to optimize performance and placement, ensuring reliable, scalable compute for rapid research iteration.

Qualifications

• Experience operating large-scale GPU clusters and container orchestration systems.
• Strong systems background: Linux, networking, storage, infra-as-code.
• Knowledge of cloud platforms (GCP, AWS, Azure) and ML/AI services.
• Understanding of monitoring, logging, observability, and version control for ML.
• Familiarity with CUDA/NCCL and distributed workload profiling.
• Owns deliverables end-to-end, from requirements through autonomous execution.

Responsibilities

• Design, deploy, and operate large distributed GPU clusters end to end.
• Extend scheduling and orchestration for multi-tenancy across workloads.
• Build software that abstracts cluster management for researchers and engineers.
• Own cluster storage and artifact paths with retention and lineage.
• Monitor and improve reliability and error recovery; build observability.
• Partner with researchers to unblock large-scale runs and advise on placement.

Skills

GPU clusters operations

Kubernetes

Slurm

Docker

Linux

Networking

Infrastructure as code

Cloud platforms (GCP, AWS, Azure)

Monitoring & observability

Version control for ML

CUDA/NCCL

Performance profiling

End-to-end ownership

Tools

Kubernetes

Slurm

Docker

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. Everything we do — training, evaluation, serving — runs on our GPU fleet. Your mission is to design, build, and operate the supercomputing environment underneath it all, delivering performant, reliable, and cost-efficient compute to ensure research is able to iterate rapidly at scale.

Responsibilities

• Design, deploy, and operate large distributed GPU clusters end to end: provisioning, imaging, upgrades, and capacity planning

• Extend scheduling and orchestration systems (e.g. Kubernetes, Slurm) for topology‑aware placement, preemption, quotas, and multi‑tenancy across training and inference workloads

• Build software that abstracts cluster management and presents a unified, self‑serve interface to researchers and engineers

• Own cluster storage and artifact paths for checkpoints and logs, with clear retention and lineage

• Monitor and continuously improve reliability and error recovery; build the observability to catch failures before researchers do

• Partner with researchers to unblock large‑scale runs and advise on performance and placement trade‑offs

What we’re looking for

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

• Experience operating large-scale GPU clusters and container orchestration frameworks (e.g. Kubernetes, Slurm, Docker)

• Strong systems background: Linux, networking, storage, infrastructure‑as‑code

• Knowledge of cloud platforms (GCP, AWS, or Azure) and their ML/AI service offerings

• Understanding of monitoring, logging, observability, and version control best practices for ML systems

• Familiarity with CUDA/NCCL and performance profiling for distributed workloads

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