Platform Engineer (Full-time)
Apply NowCompany: Strong Compute
Location: San Francisco, CA 94112
Description:
We're building the operating system for AI compute-seamless workstation style access as a single entry point into global compute, with ultra fast data transit connecting everything.
If you love high-performance computing, distributed systems, and AI infrastructure, and have experience managing large-scale GPU clusters and storage systems, you'll fit right in.
What you'll work on:
Scalable, distributed AI infrastructure across cloud, on-prem, and colocation environments
GPU orchestration and fault-tolerant scheduling (Slurm, Kubernetes, Ray, and other orchestration frameworks)
Supercomputing clusters and high-performance storage solutions for AI workloads
Ultra-fast data pipelines for petabyte-scale AI training workloads
Multi-cloud orchestration and on-premise AI data centers, making compute feel like a single, unified system
DevOps & MLOps automation for streamlined model training and deployment
Security and reliability for distributed computing across the public internet
Scaling compute clusters 10-20x, from 128 to 1024+ GPUs, ensuring high uptime, reliability, and utilization
Optimizing HPC clusters for AI training, including upgrade pathways and cost-efficiency strategies
Your background would include some or all of the following:
Strong systems engineering skills with experience in distributed computing and storage for AI workloads
Proficiency in GPU cluster management, including NVIDIA GPUs, Slurm, and Kubernetes
Deep understanding of distributed training frameworks and multi-cloud architectures (AWS, GCP, Azure, and emerging GPU clouds)
Experience managing large-scale clusters, including team leadership, hiring, and scaling operations
Expertise in high-performance storage (Ceph, S3, ZFS, Lustre, and others) for massive AI datasets
Ability to optimize cluster utilization, uptime, and scheduling for cost-effective operations
Understanding of colocation strategies, managing AI data centers, and running HPC workloads in mixed environments
DevOps/MLOps experience, automating training pipelines for large-scale AI models
Experience working with AI/ML researchers, optimizing infrastructure for deep learning training
This role is perfect for senior engineers who have built and scaled large AI compute clusters and are passionate about pushing the boundaries of distributed computing and AI training infrastructure.
Our culture
We move fast. We ship weekly-new features, improvements, and fixes go live fast.
We test big. Every month, we stress test with large groups of users face to face, get real-world feedback, and iterate rapidly. We build together. On site only, in SF or Sydney.
We iterate relentlessly. Direct user feedback shapes our roadmap-we release, test, refine, and keep moving.
We travel when needed. Engineers may travel between SF and Sydney to run events and meet with clients.
Location: SF or Sydney (OG startup house vibe, great food, late nights, all the GPUs)
Equipment & Benefits:
Top spec Macbook + separate GPU cluster dev environments for each engineer.
Weekly cash bonus when you workout out 3+ times a week.
Comprehensive health benefits, including a choice of Kaiser, Aetna OAMC, and HDHP (HSA-eligible) plans for our SF-based team members.
Highest in the world 20 year exercise window for options
Don't have all the skills? Apply anyway! We're looking for people who move fast, learn fast, and ship fast. If that's you, let's talk.
Want to get to know us first? Attend one of our upcoming events.
If you love high-performance computing, distributed systems, and AI infrastructure, and have experience managing large-scale GPU clusters and storage systems, you'll fit right in.
What you'll work on:
Scalable, distributed AI infrastructure across cloud, on-prem, and colocation environments
GPU orchestration and fault-tolerant scheduling (Slurm, Kubernetes, Ray, and other orchestration frameworks)
Supercomputing clusters and high-performance storage solutions for AI workloads
Ultra-fast data pipelines for petabyte-scale AI training workloads
Multi-cloud orchestration and on-premise AI data centers, making compute feel like a single, unified system
DevOps & MLOps automation for streamlined model training and deployment
Security and reliability for distributed computing across the public internet
Scaling compute clusters 10-20x, from 128 to 1024+ GPUs, ensuring high uptime, reliability, and utilization
Optimizing HPC clusters for AI training, including upgrade pathways and cost-efficiency strategies
Your background would include some or all of the following:
Strong systems engineering skills with experience in distributed computing and storage for AI workloads
Proficiency in GPU cluster management, including NVIDIA GPUs, Slurm, and Kubernetes
Deep understanding of distributed training frameworks and multi-cloud architectures (AWS, GCP, Azure, and emerging GPU clouds)
Experience managing large-scale clusters, including team leadership, hiring, and scaling operations
Expertise in high-performance storage (Ceph, S3, ZFS, Lustre, and others) for massive AI datasets
Ability to optimize cluster utilization, uptime, and scheduling for cost-effective operations
Understanding of colocation strategies, managing AI data centers, and running HPC workloads in mixed environments
DevOps/MLOps experience, automating training pipelines for large-scale AI models
Experience working with AI/ML researchers, optimizing infrastructure for deep learning training
This role is perfect for senior engineers who have built and scaled large AI compute clusters and are passionate about pushing the boundaries of distributed computing and AI training infrastructure.
Our culture
We move fast. We ship weekly-new features, improvements, and fixes go live fast.
We test big. Every month, we stress test with large groups of users face to face, get real-world feedback, and iterate rapidly. We build together. On site only, in SF or Sydney.
We iterate relentlessly. Direct user feedback shapes our roadmap-we release, test, refine, and keep moving.
We travel when needed. Engineers may travel between SF and Sydney to run events and meet with clients.
Location: SF or Sydney (OG startup house vibe, great food, late nights, all the GPUs)
Equipment & Benefits:
Top spec Macbook + separate GPU cluster dev environments for each engineer.
Weekly cash bonus when you workout out 3+ times a week.
Comprehensive health benefits, including a choice of Kaiser, Aetna OAMC, and HDHP (HSA-eligible) plans for our SF-based team members.
Highest in the world 20 year exercise window for options
Don't have all the skills? Apply anyway! We're looking for people who move fast, learn fast, and ship fast. If that's you, let's talk.
Want to get to know us first? Attend one of our upcoming events.