Jobs at Graphcore

The Lead Analytics Engineer at Graphcore is responsible for developing and managing the analytics engineering layer, creating robust datasets and metrics, and collaborating with business stakeholders to enhance decision-making through reliable analytics.

The Senior Technical Program Manager will oversee project planning and delivery, ensure team alignment, monitor dependencies, and enhance processes in AI systems development.

Develop, implement and optimise large-scale ML models for Graphcore hardware; benchmark and scale training/inference across thousands of accelerators; test internal releases, fix and review code; design and run experiments; collaborate with Research, Software and Product teams; engage with the AI community.

As a Signal Integrity Engineer, you will manage high-speed PCB design, enhance digital interfaces, and validate structures for various advanced interfaces, transforming complex requirements into actionable design rules.

Develop power integrity solutions for AI compute systems, analyze power delivery networks, optimize decoupling strategies, and influence design improvements.

Lead discovery and design for large transformation programmes: map current processes, define requirements, design future-state workflows, analyse systems and data, support testing and implementation, and drive stakeholder alignment to ensure successful adoption and measurable business outcomes.

Operate and extend OpenStack-based private and public cloud services, deploy end-user services using Infrastructure-as-Code, automate observability and metrics collection, configure and benchmark AI hardware and third-party servers/switches/storage, collaborate with datacenter operations and engineering to maintain high-performance fleets, and drive corrective actions and integrations for cloud reference designs.

Deploy, operate and optimise Kubernetes-managed services across private and public clouds supporting Graphcore AI systems. Work with datacentre teams to configure, validate and benchmark hardware; implement Infrastructure-as-Code and continuous deployment; manage monitoring, storage and virtual networks; and support internal users and product teams to ensure peak performance and availability.

The Signal Integrity Engineer will develop and simulate PCB structures to ensure high-speed SERDES and DDR interfaces perform correctly, collaborating with CAD engineers and defining material specifications for optimal performance.

Validate and benchmark ML models and stacks on Graphcore hardware. Build automated benchmarking pipelines, run open-source models, create targeted tests for numerical precision, quantisation, distributed execution and subgraphs, and debug performance and correctness across frameworks and execution environments.

Validate and benchmark ML models on Graphcore's software/hardware stack. Create automated benchmarking pipelines, run open-source models, design targeted tests for numerical precision, quantisation, attention, distributed execution, and debug performance and correctness issues across frameworks and environments.

Design and build test infrastructure, automation, and tooling to validate ML software stacks and hardware; shape test architecture across CI pipelines, frameworks, simulators, emulators, and large ML workloads; mentor engineers and own complex validation projects.

Develop and maintain internal build, test, and integration tooling for the ML software stack. Build release processes, assist dependency management, guide developers on build best practices, collaborate with QA and engineers to resolve integration issues, and contribute fixes to the ML software stack.

Design, deploy, and maintain tools and services for build, CI/CD, packaging, and releases. Manage cloud and Kubernetes infrastructure, use IaC, reduce toil, and support junior engineers for reliable ML software delivery.

Build and operate Kubernetes-based infrastructure and tooling to support development, testing, deployment and scaling of Graphcores ML software. Manage cloud infrastructure, CI/CD practices, and collaborate with software teams to improve reliability and observability of HPC AI platforms.

Build, operate, and maintain infrastructure and tools for ML teams: deploy services with Kubernetes/Docker, manage cloud infrastructure (Terraform), support CI/CD and ML workflows, and integrate observability and accelerator tooling.

The Senior Systems Engineer focuses on analyzing performance data from systems, ensuring stability for production, and using engineering judgment for decision-making in a high-performance computing context.

The Research Engineer will advance AI research, implement ML experiments, collaborate with engineers, and publish findings at conferences.

Develop and deploy cloud services, optimize AI systems, and work on infrastructure automation while collaborating with various teams.

As a Staff Software Engineer (ML QA), you'll design and maintain testing infrastructure for the ML software stack, focusing on automation, software quality, and mentoring junior engineers in a collaborative environment.