Software Engineer, Data Infrastructure

About the Role

The Data Infrastructure team at Cohere is responsible for the storage and data movement layer underlying every model training run. We're building the unified storage layer that feeds our training workloads. It needs to serve petabytes of training data and model checkpoints fast enough to keep thousands of GPUs busy across several training clusters. In this role, you’d have an opportunity to build this system from the ground up as a key contributor working at massive scale.

Responsibilities

• Design, build, and operate the distributed storage system that feeds model training and evaluation.
• Run this system on multiple Kubernetes clusters at petabyte scale.
• Work with researchers and training-infra teams on how jobs read and write data, translating that into throughput, latency, and durability requirements.
• Work through the networking, I/O, and consistency problems of moving large datasets and checkpoints across regions and backends, with GPU idle time and time-to-insight as the measures of success.

Qualifications

Required

• Strong storage fundamentals, including replication, consistency, caching, and data lifecycle management.
• Strong coding ability in Python or Go, with a willingness to pick up the other.
• Experience running stateful systems on Kubernetes, including Persistent Volumes, CSI drivers, and StatefulSets.
• Hands-on experience with cloud object storage such as Amazon S3 as well as POSIX-style filesystems.

Preferred

• Experience with parallel or HPC filesystems such as Weka, VAST, or Lustre.
• Familiarity with the data-loading and checkpointing patterns used in large-scale model training.
• An interest in how large models are trained and evaluated, including trajectory and eval data, and a desire to understand the workloads the platform serves.