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# Staff Software Engineer, Inference Infrastructure

- **Company:** COHERE, LLC
- **Location:** New York, NY, United States (Remote available)
- **Experience:** Expert
- **Salary:** $156,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, Linux, Distributed Systems, Graphics Processing Unit (GPU), Google Cloud, System Availability, Large Language Models, Multi-Cloud, AI Platforms, Kubernetes, Low Latency, Machine Learning Operations, Oracle Cloud Infrastructure, Web Api
- **Published:** November 6, 2026
- **Apply:** https://www.juju.com/job/7_A61120721802980A40DD3E5712C8974A

## About the Role

You will also get the opportunity to interface with customers and create customized deployments to meet their specific needs.You may be a good fit if you have:5+ years of engineering experience running production infrastructure at a large scaleExperience designing large, highly available distributed systems with Kubernetes, and GPU workloads on those clustersExperience with Kubernetes dev and production coding and supportExperience with GCP, Azure, AWS, OCI, multi-cloud on-prem / hybrid servingExperience in designing, deploying, supporting, and troubleshooting in complex Linux-based computing environmentsExperience in compute/storage/network resource and cost managementExcellent collaboration and troubleshooting skills to build mission-critical systems, and ensure smooth operations and efficient teamworkThe grit and adaptability to solve complex technical challenges that evolve day to dayFamiliarity with computational characteristics of accelerators (GPUs, TPUs, and/or, custom

## Description

Do you want to help define and build the next generation of AI platforms powering advanced NLP applications?
We are looking for Members of Technical Staff 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.