We are seeking an AI Networking Architect to join the Networking Research Group. This role bridges the gap between emerging AI workloads and the data center infrastructure that powers them, working at the intersection of AI applications, distributed systems, networking hardware, and software architecture. You will join a focused team of multidisciplinary engineers driving AI workload optimization through deep application understanding, network analysis, and end-to-end systems thinking. Your insights will directly shape our products across the full stack - from applications and software libraries to hardware architecture and physical design.
What you'll be doing:
Model the performance of complex AI workloads to identify bottlenecks and recommend system-level optimizations.
Analyze new AI models, distributed training techniques, and inference workloads to understand their infrastructure requirements.
Build simulation and hardware platforms, run real AI workloads on them, and develop analytical tools to evaluate trade-offs across compute, memory, storage, and network behavior.
Translate research insights and workload behavior into actionable software, xhardware, and networking architecture requirements.
Partner with architecture, software, and product teams to influence our future networking and AI infrastructure roadmaps.
Drive architectural innovation by applying deep workload analysis to production machine learning frameworks.
Requirements: What we need to see:
B.Sc. or M.Sc. in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
5+ years of relevant industry or research experience. This is not an entry-level position; coursework and personal projects do not substitute for production or research experience at scale.
Hands-on experience running and analyzing AI workloads on multi-node systems - distributed training or large-scale inference - including measuring where time and resources are actually spent.
Demonstrated performance analysis work: building analytical or simulation models of real systems, validating them against measurement, and identifying bottlenecks that led to design or deployment changes.
Strong systems-level thinking across the full AI stack, from model and framework behavior down through compute, memory, storage, and network.
Track record of translating research findings and workload analysis into concrete software and hardware specifications that engineering teams acted on.
Strong programming skills in Python and C/C++, applied to performance modeling, data analysis, and prototyping.
Ways to Stand Out from the crowd:
Deep understanding of data centers, network topologies, and communication protocols.
Familiarity with GPU clusters, collective communication, storage systems, and AI networking bottlenecks.
Experience with distributed training, distributed inference, or large-scale AI serving systems, including the performance metrics and deployment strategies that govern them.
Experience in agentic programming and AI tooling.
Track record of turning academic research into concrete software, hardware, or architecture requirements, and of leading complex multidisciplinary projects with measurable production impact.
This position is open to all candidates.