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Senior Platform Architect

Axelera
CompanyAxelera
CategoryEngineering
LocationHybrid/Remote - Europe (incl. UK)
RemoteRemote
EmploymentNot stated
LevelSenior
SalaryNot stated by the employer
Posted26 Jun 2026
Last verified30 Jul 2026
SourceEmployer career page (ashby)
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Description
About Us Axelera AI https://www.axelera.ai is not your regular deep-tech startup. We are creating the next-generation AI platform to support anyone who wants to help advancing humanity and improve the world around us. In just four years, we have raised a total of $370 million and have built a world-class team of 220+ employees (including 49+ PhDs with more than 40,000 citations), both remotely from 18 different countries and with offices in Belgium, France, Switzerland, Italy, the UK, headquartered at the High Tech Campus in Eindhoven, Netherlands. We have also launched our Metis™ AI Platform, which achieves a 3-5x increase in efficiency and performance, and have visibility into a strong business pipeline exceeding $100 million. Our unwavering commitment to innovation has firmly established us as a global industry pioneer. Are you up for the challenge? Position Overview We are looking for a Senior Platform Architect to own the platform-level architecture for our datacenter AI accelerator systems — from server through rack integration and, eventually, full datacenter deployment. The role sits within the System Architecture team, and marks our expansion from edge AI into the datacenter. You’ll take compute, memory, and connectivity requirements defined by the broader System Architecture team and translate them into architectural requirements on our hardware platforms. You own how accelerators are composed into systems to execute workloads effectively, defining system topology, host integration, memory organization and platform-level requirements to achieve performance, scalability and operability. The role is primarily in close collaboration with the AI Infrastructure Systems (AIIS) division that builds our board- and system-level products, as well as with the silicon and software divisions. Finally, you will be engaged with strategic customers and ecosystem partners on architecture requirements and technical alignment. Key responsibilities: - Define the platform-level architecture for our datacenter AI accelerator systems, spanning server board design, rack integration, and datacenter-level deployment. - Translate system-level compute, memory, and interconnect requirements — defined in collaboration with the broader System Architecture team and product division — into concrete hardware platform specifications. - Specify physical interconnect infrastructure — defining the architectural requirements and trade-offs that the AI Infrastructure Systems division executes against, including how interconnect characteristics impact system-level workload performance. - Define host, storage and networking integration and organization at the hardware level, covering PCIe and CXL, as well as system-level power budgeting. - Ensure scaled-up and scaled-out designs sustain target performance as systems grow from single nodes to large clusters. - Define operability and RAS (reliability, availability, serviceability) requirements across redundancy architecture, hot-swap capability, telemetry and management interfaces (BMC/IPMI/Redfish), and fault containment to ensure platforms are manageable and dependable in production. - Take a leading technical role in the system architecture team, interfacing directly with key partners and internal stakeholders to align architecture decisions, including the AI Infrastructure Systems division, silicon, product management, software, and customers, system integrators and ecosystem partners on architecture requirements — with commercial engagement and design ownership sitting with the AIIS Director. - Drive methodology and best practices for platform-level design as the team scales. Qualifications: - Experience: Significant experience (5+ years) in system, platform, or hardware architecture, with a strong track record at server and/or rack scale. - Core knowledge: Scale-up and scale-out system design, distributed workload mapping, functional partitioning, and in
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