We are seeking an AI Networking Exploration Architect for our Networking Insights Group to bridge the gap between cutting-edge, hyper-scale AI workloads and the datacenter infrastructure that enables them. You will join a small, focused team of multidisciplinary engineers driving AI workload optimization through deep application understanding 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.
Translate state-of-the-art research into actionable infrastructure, software, and hardware features in partnership with architecture teams.
Rapidly master new AI domains (LLMs, generative models, multimodal systems) and distill key findings for product teams.
Incorporate your deep knowledge of AI applications into our hardware and software roadmaps.
Conduct independent research by formulating hypotheses about workload behavior and validating them through rigorous analysis.
Drive architectural innovation and network optimization by applying your domain expertise to exploratory analysis of real-world Deep Learning (DL) workloads.
Requirements: What we need to see:
M.Sc. or Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, or equivalent experience.
+5 years of experience.
Strong ML/Data Science background with hands-on experience in LLMs or generative AI.
A systems-level mindset with the ability to estimate end-to-end requirements across the entire AI stack.
Proven ability to translate research and product requirements into clear software/hardware specifications.
Exceptional research skills: you can digest academic papers, self-learn new domains, and independently test hypotheses.
Advanced Python programming skills for performance modeling and data analysis.
Excellent communication skills, with the ability to present complex findings with clarity and conviction.
A pragmatic approach: you are detail-oriented but can prioritize effectively to focus on the most critical issues.
Ways to Stand Out from the Crowd:
Deep understanding of datacenter infrastructure, network topologies, and protocols.
Expertise in distributed training methods and their impact on infrastructure.
Knowledge of AI performance metrics and the impact of different deployment strategies.
Experience extrapolating academic research into tangible hardware architecture requirements.
A track record of leading complex, multidisciplinary research projects that result in production impact.
This position is open to all candidates.