Site Reliability Engineer, Inference Infrastructure

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 role?

Are you energized by building high-performance, scalable and reliable machine learning systems? Do you want to help define and build the next generation of AI platforms powering advanced NLP applications? We are looking for a Site Reliability Engineer to join the Model Serving team at Cohere. The team is responsible for developing, deploying, and operating the AI platform delivering Cohere's large language models through easy to use API endpoints. In this role, you will work closely with many teams to deploy optimized NLP models to production in low latency, high throughput, and high availability environments. You will also get the opportunity to interface with customers and create customized deployments to meet their specific needs.

As a Site Reliability Engineer you will:

Build self-service systems that automate managing, deploying and operating services.

This includes our custom Kubernetes operators that support language model deployments.

Automate environment observability and resilience. Enable all developers to troubleshoot and resolve problems.

Take steps required to ensure we hit defined SLOs, including participation in an on-call rotation.

Build strong relationships with internal developers and influence the Infrastructure team’s roadmap based on their feedback.

Develop our team through knowledge sharing and an active review process.

You may be a good fit if you have:

5+ years of engineering experience running production infrastructure at a large scale

Experience designing large, highly available distributed systems with Kubernetes, and GPU workloads on those clusters

Experience with Kubernetes dev and production coding and support

Experience with GCP, Azure, AWS, OCI, multi-cloud on-prem / hybrid serving

Experience in designing, deploying, supporting, and troubleshooting in complex Linux-based computing environments

Experience in compute/storage/network resource and cost management

Excellent collaboration and troubleshooting skills to build mission-critical systems, and ensure smooth operations and efficient teamwork

The grit and adaptability to solve complex technical challenges that evolve day to day

Familiarity with computational characteristics of accelerators (GPUs, TPUs, and/or custom accelerators), especially how they influence latency and throughput of inference.