Software Engineer, ML Workload Platform

About the Role

WHO ARE WE? Cohere is the leading security-first enterprise AI company. We build cutting-edge foundation AI models and end-to-end products that are designed to solve real-world business problems. We’re training and deploying frontier models for enterprises who are building AI systems. We believe that our work is instrumental to the widespread adoption of AI and we are looking for folks that want to be part of that. We obsess over what we build. Each one of us is responsible for contributing to increasing the capabilities of our models and the value they drive for our customers. Cohere is a team of researchers, engineers, designers, and more, who are all passionate about their craft. We are a global technology company headquartered in Toronto with key offices in London, New York City, San Francisco, Montreal, Paris, Berlin and Seoul. Join us! 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, i

Key Responsibilities

• 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, i

Requirements & Qualifications

• Strong problem solving foundations, knowledge of modern engineering practices, and interest in technology systems.

Perks & Benefits

• Official mentor support
• verified hiring timeline
• and career progression.