> Markdown version of [/jobs/ext/1901306-infrastructure-engineers](https://www.wearedevelopers.com/jobs/ext/1901306-infrastructure-engineers). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # infrastructure engineers - **Company:** CAUSAL LABS, INC. - **Location:** San Francisco, CA, United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Computer Clusters, Configuration Management, Nvidia CUDA, Linux, Reliability Engineering, Software Engineering, Supercomputing, Data Logging, Graphics Processing Unit (GPU), Kubernetes, Slurm, Machine Learning Operations, Software Version Control, Docker - **Published:** July 31, 2026 - **Apply:** https://www.careerbuilder.com/job-details/member-of-technical-staff-compute-cluster-san-francisco-ca--78daf75a-2308-48d3-a881-4ea19da0b2a5 ## About the Role 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 Skills: Amazon Web Services (AWS), Artificial Intelligence (AI), Best Practices, CUDA (Compute Unified Device Architecture), Capacity Management, Cloud Computing, Continuous Improvement, Docker, Drug Discovery, Error Recovery, GCP (Good Clinical Practices), GPU (Graphics Processing Unit), Microsoft Windows Azure, Particle Physics, Physics, Problem Solving Skills, Reliability Engineering, Research Skills, Robotics, Source Code/Configuration Management (SCM), Supercomputing, Vehicle Fleets ## 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 ## Related Videos - [Running Secure Life Science Research at Scale using Hybrid GPU HPC and Kubernetes 🧬](https://www.wearedevelopers.com/videos/100355-running-secure-life-science-research-at-scale-using-hybrid-gpu-hpc-and-kubernetes) - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Docker exec without Docker](https://www.wearedevelopers.com/videos/1094-docker-exec-without-docker) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) ## Related Articles - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Best US AI Conferences for CTOs in 2026: Build vs. Buy, Vendor Evaluation, and Peer Intelligence](https://www.wearedevelopers.com/magazine/736-best-us-ai-conferences-for-ctos-in-2026-build-vs-buy-vendor-evaluation-and-peer-intelligence) - [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)