We are seeking a Network Topology Researcher with a focus on designing and optimizing novel network topologies for AI training and inference workloads.
This role is ideal for candidates with a deep understanding of existing types of network topologies, such as Clos and Mesh, and who are passionate about researching and developing new architectures to improve efficiency, scalability, and performance in AI systems.
Responsibilities:
Conduct cutting-edge research, design, and evaluate new network topologies optimized for AI training and inference workloads, and also their impact on legacy workloads
Explore innovative approaches to improve network performance, including latency, bandwidth, and fault tolerance
Collaborate with infrastructure teams to integrate new topologies into large-scale AI systems
Conduct simulations and experiments to validate and benchmark novel network designs
Stay current with the latest developments in network architecture and AI workload optimization
Write white papers on current developments and future research directions
File patents, produce and present research papers at top-tier conferences
What we offer:
Opportunity to lead cutting-edge projects in one of the most dynamic sectors of the tech industry
Work closely with a leading professor
Exposure to conferences, courses, and connection opportunities to the top-tier experts
Access to cutting-edge tools and technologies
This is an exciting opportunity for a motivated researcher to contribute to the cutting-edge of AI infrastructure and help shape the future of AI network architectures.
Requirements: Proven experience in network topology types research and/or graph theory research
Strong interpersonal skills, with a collaborative spirit and the ability to work independently
Strong analytical and problem-solving skills, ability to think outside the box, with the ability to innovate in a fast-evolving field
Excellent intercultural communication, coordination, presentation, and reporting skills, with the ability to work effectively in a research environment
Skills
How would you stand out:
PhD or MSc in Computer Science, Electrical Engineering, Math, or related field
Past experience in graph theory is a significant advantage
Familiarity with the challenges of AI training and inference workloads, including high-throughput
data pipelines
Knowledge of LMMs
Hands-on experience with network simulation tools and performance evaluation frameworks
Expertise in distributed systems and large-scale AI infrastructure.
This position is open to all candidates.