Staff Engineer • Senior

Who you are

You have extensive experience in designing and delivering large-scale storage systems, particularly for AI and machine learning workloads. Your background includes architecting high-performance parallel filesystems and object stores, and you are adept at integrating cutting-edge technologies such as WekaFS, Ceph, and Lustre. You have a strong understanding of cost optimization strategies, routinely achieving significant savings through intelligent tiering and lifecycle policies. Your technical skills extend to building Kubernetes-native storage operators and self-service platforms that ensure automated provisioning and performance isolation at scale.

You are familiar with optimizing end-to-end data paths for high throughput, capable of delivering 10-50 GB/s per node. Your expertise includes designing multi-tier caching architectures and implementing intelligent prefetching and model-weight distribution. You thrive in environments where you can troubleshoot bottlenecks and optimize TCP/IP for storage, ensuring maximum efficiency and minimal latency.

Desirable

Experience with RDMA and InfiniBand networks is a plus, as is familiarity with NVMe-oF and iSCSI protocols. You are comfortable working with large datasets and have a keen eye for capacity forecasting and right-sizing storage solutions. Your collaborative spirit allows you to work effectively with cross-functional teams, driving projects from conception through to successful implementation.

What you'll do

In this role, you will be responsible for designing and delivering multi-petabyte AI/ML storage systems that meet the demands of the world’s largest AI training and inference workloads. You will lead the integration of advanced storage technologies, ensuring that systems are optimized for performance and cost. Your day-to-day tasks will include architecting high-performance parallel filesystems and object stores, as well as evaluating new technologies to enhance system capabilities.

You will also focus on capacity planning and cost optimization, routinely achieving 30-50% savings through intelligent tiering and lifecycle policies. Your role will involve designing and optimizing RDMA and InfiniBand networks, tuning them for maximum throughput and minimum latency. You will implement NVMe-oF and iSCSI protocols, troubleshooting any bottlenecks that arise to maintain system integrity and performance.

Building Kubernetes storage operators and controllers will be a key part of your responsibilities, enabling automated provisioning and creating self-service abstractions for users. You will ensure strict multi-tenancy and quota enforcement at cluster scale, allowing for efficient resource management across multiple users and applications.

Your work will directly impact the performance of AI workloads, as you will be tasked with delivering 10-50 GB/s per GPU node. You will optimize caching strategies for weights, datasets, and checkpoints, ensuring that data paths are efficient and effective. Your ability to troubleshoot and optimize parallel filesystems will be crucial in maintaining high performance for AI applications.

What we offer

Together AI offers a collaborative and innovative work environment where your contributions will have a significant impact on the future of AI infrastructure. You will have the opportunity to work with cutting-edge technologies and be part of a team that is dedicated to pushing the boundaries of what is possible in AI and machine learning. We encourage you to apply even if your experience doesn't match every requirement, as we value diverse perspectives and backgrounds.

We provide competitive compensation and benefits, along with opportunities for professional growth and development. Join us in shaping the future of AI infrastructure and making a difference in the world of technology.