Software Engineer, ML Workload Platform

Who are we?

Why this team?

Internal Infrastructure builds the infrastructure and tools used to train, evaluate, and serve Cohere's foundation models. You will work with AI researchers and ML engineers on stability, scalability, and observability. The work accelerates the models behind Cohere's platform, North.

You will build the platform that runs training, evaluation, and reinforcement-learning workloads: job lifecycle, scheduling, workload networking, and the storage those jobs depend on. The role includes a compensated 24x7 on-call rotation.

What you will do

• Own the platform that schedules and runs training, evaluation, and reinforcement-learning jobs on Kubernetes: who gets capacity, what happens when a job fails, and how a researcher finds out whether the platform caused it.
• Build the networking and runtime isolation those workloads need, including agent and sandbox environments.
• Work with the data infrastructure team on storage for checkpoints and other training artifacts.
• Troubleshoot production failures and performance problems, and leave the system easier to operate than you found it.
• Build automation, infrastructure-as-code, and observability so the platform stays maintainable.

We hire at more than one level. We set level based on the scope you've owned and how independently you drive work.

You may be a good fit if you have

• Run a job platform, batch system, or Kubernetes controllers in production. Scheduling, queues, and multi-tenant fairness are central to this work.
• Shown distributed systems judgment: retries, partial failure, and knowing what not to automate.
• Worked on ML training workflows, RL sandboxes, container networking (CNI), or checkpoint storage at scale. Direct GPU-cluster experience is a plus.
• Written software in Python or Go. You would rather extend a solid open-source project than rebuild it.
• Worked with researchers, ML engineers, or another team whose goals were not identical to yours.
• Found a bottleneck, proposed a fix, and driven it in a fast-moving environment.

If this does not line up exactly with your experience, we still encourage you to apply.