Cloud Software Engineer - ML Infrastructure

at  graphcore

ABOUT GRAPHCORE

How often do you get the chance to build a technology that transforms the future of humanity? Graphcore products have set the standard in made-for-AI compute hardware and software, gaining global attention and industry acclaim. Now we are developing the next generation of artificial intelligence compute with systems that will allow AI researchers to develop more advanced models, help scientists unlock exciting new discoveries, and power companies around the world as they put AI at the heart of their business. Graphcore recently joined SoftBank Group – bringing large and ongoing investment from one of the world’s leading backers of innovative AI companies.

JOB SUMMARY

As a Cloud Software Engineer, you will play a critical role in enabling new AI accelerator hardware within Kubernetes environments. This position focuses on investigating how to integrate the new generation of our hardware into key MLOps technologies from the Kubernetes ecosystem, such as Kubeflow,Volcano, Kueue, and others. Additionally, you will be responsible for developing Go applications, ensuring a seamless and native Kubernetes end-user experience. While Kubernetes integration is the primary responsibility, this role emphasizes the importance of MLOps and machine learning (ML) knowledge to effectively support the development of K8s integration. Please be aware that this is not a DevOps position, read on for further details on the role requirements.

ESSENTIAL QUALIFICATIONS

• Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent experience.
• At least 3 years of experience in software development.
• Experience with machine learning technologies in the Kubernetes ecosystem, such as Kubeflow, Volcano, and Kueue.
• Familiarity with MLOps workflows and tools for deploying and managing machine learning workloads.
• Working knowledge of Go or Python programming language.
• Expertise in Kubernetes, including resource management and scaling.
• Familiarity with Linux.
• English proficiency at a B2 level.

PREFERRED QUALIFICATIONS

• Knowledge of distributed training frameworks and techniques for scaling machine learning workloads.
• Hands-on experience with cloud platforms such as AWS, Azure, or GCP, including their machine learning services.
• Knowledge of container orchestration and cloud-native development.
• Familiarity with CI/CD pipelines and DevOps tools like GitHub or GitLab.

• Investigate integration strategies for our AI accelerator hardware with MLOps technologies in the Kubernetes ecosystem, such as Kubeflow, Volcano, Kueue, and others.
• Develop and maintain applications in Go for integrating new AI accelerator hardware into Kubernetes environments.
• Ensure seamless hardware integration with Kubernetes clusters, delivering a native end-user experience.
• Participate in code reviews, design discussions, and troubleshooting sessions to enhance system reliability and performance.
• Maintain high software quality standards by following best practices.
• Write and maintain comprehensive documentation for code, projects, and integration guides.
• Stay up-to-date with the latest trends in Kubernetes, AI/ML technologies, and MLOps workflows to ensure the platform remains cutting-edge.