Top AI & Machine Learning Jobs

Develop and maintain internal build, test, and integration tooling for the ML software stack. Improve release processes, guide developers on dependency and build best practices, collaborate with QA and dev teams to resolve integration issues, and contribute fixes to the ML software stack in Linux environments.

Define and drive the software architecture for Graphcore's ML accelerator stack. Collaborate with HW and software teams to document, communicate, prototype, and maintain end-to-end architecture, ensuring performance, reliability, maintainability, security, and alignment with product goals.

Build and maintain infrastructure, tools and services to support AI research and engineering. Deploy and operate services using Kubernetes/Docker, manage cloud infrastructure with Terraform, support CI/CD, and enable ML application deployment and accelerator hardware management.

Build, own, and maintain infrastructure, tools, and services to support ML research and engineering. Deploy and maintain services with Kubernetes and Docker, manage cloud infrastructure (Terraform), and improve CI/CD, build, test, deployment, and productisation of ML software on HPC AI platforms.

Build and maintain infrastructure, tools and services to support AI research and engineering. Deploy and operate services with Kubernetes and Docker, manage cloud infrastructure (Terraform/AWS), support CI/CD, and enable ML application deployment, orchestration, and accelerator hardware management.

Design and run ML experiments, implement and optimize algorithms for hardware efficiency, collaborate with researchers and engineers, and publish and present results at AI conferences.

Conduct hardware-aware AI research: design and run ML experiments, implement performant, scalable implementations (low-level optimizations), collaborate with silicon and software teams, and publish/present results at conferences.

Build production-grade Go services and Kubernetes components (CRDs/operators) to expose accelerator hardware for scalable AI compute. Ensure reliability, performance, and observability; collaborate with hardware, cloud, and ML teams; lead technical initiatives and mentor engineers.

Conduct hardware-aware AI research: generate ideas, design and run ML experiments, implement performant scalable systems, publish and present results, and collaborate with researchers, silicon and software engineers to guide next-generation AI hardware.

Lead Go-based development to integrate a new AI accelerator into Kubernetes clusters, mentor engineers, collaborate cross-functionally, troubleshoot complex issues, contribute to upstream projects, and maintain documentation.